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Mac Schwager

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58 papers

2 author rows

AAAI Conference 2026 Conference Paper

CineMPC: A Fully Autonomous Drone Cinematography System Incorporating Zoom, Focus, Pose, and Scene Composition (Abstract Reprint)

Pablo Pueyo
Juan Dendarieta
Eduardo Montijano
Ana Cristina Murillo
Mac Schwager

We present CineMPC, a complete cinematographic system that autonomously controls a drone to film multiple targets recording user-specified aesthetic objectives. Existing solutions in autonomous cinematography control only the camera extrinsics, namely, its position and orientation. In contrast, CineMPC is the first solution that includes the camera intrinsic parameters in the control loop, which are essential tools for controlling cinematographic effects such as focus, zoom, and depth of field. The system is validated in real-world experiments.

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ICRA Conference 2025 Conference Paper

A Control Barrier Function for Safe Navigation with Online Gaussian Splatting Maps

Timothy Chen
Aiden Swann
Javier Yu
Ola Shorinwa
Riku Murai
Monroe Kennedy III
Mac Schwager

SAFER-Splat (Simultaneous Action Filtering and Environment Reconstruction) is a real-time, scalable, and minimally invasive safety filter, based on control barrier functions, for safe robotic navigation in a detailed map constructed at runtime using Gaussian Splatting (GSplat). We propose a novel Control Barrier Function (CBF) that not only induces safety with respect to all Gaussian primitives in the scene, but when synthesized into a controller, is capable of processing hundreds of thousands of Gaussians while maintaining a minimal memory footprint and operating at 15 Hz during online Splat training. Of the total compute time, a small fraction of it consumes GPU resources, enabling uninterrupted training. The safety layer is minimally invasive, correcting robot actions only when they are unsafe. To showcase the safety filter, we also introduce SplatBridge, an open-source software package built with ROS for real-time GSplat mapping for robots. We demonstrate the safety and robustness of our pipeline first in simulation, where our method is 20-50x faster, safer, and less conservative than competing methods based on neural radiance fields. Further, we demonstrate simultaneous GSplat mapping and safety filtering on a drone hardware platform using only on-board perception. We verify that under teleoperation a human pilot cannot invoke a collision. Our videos and codebase can be found at https://chengine.github.io/safer-splat.

ICRA Conference 2025 Conference Paper

E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models

Chan Kim
Keonwoo Kim
Mintaek Oh
Hanbi Baek
Jiyang Lee
Donghwi Jung
Soojin Woo
Younkyung Woo

Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods are primarily designed for static environments and do not leverage the agent's own experiences to refine its initial plans. Given that real-world environments are inherently stochastic, initial plans based solely on LLMs' general knowledge may fail to achieve their objectives, unlike in static scenarios. To address this limitation, this study introduces the Experience-and-Emotion Map (E2Map), which integrates not only LLM knowledge but also the agent's real-world experiences, drawing inspiration from human emotional responses. The proposed methodology enables one-shot behavior adjustments by updating the E2Map based on the agent's experiences. Our evaluation in stochastic navigation environments, including both simulations and real-world scenarios, demonstrates that the proposed method significantly enhances performance in stochastic environments compared to existing LLM-based approaches. The code and supplementary materials are available at https://e2map.github.io/.

IROS Conference 2025 Conference Paper

GRaD-Nav: Efficiently Learning Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics

Qianzhong Chen
Jiankai Sun
Naixiang Gao
JunEn Low
Timothy Chen
Mac Schwager

Autonomous visual navigation is an essential element in robot autonomy. Reinforcement learning (RL) offers a promising policy training paradigm. However, existing RL methods suffer from high sample complexity, poor sim-to-real transfer, and limited runtime adaptability. These problems are particularly challenging for drones, with complex nonlinear and unstable dynamics, and strong dynamic coupling between control and perception. In this paper, we propose a novel framework that integrates 3D Gaussian Splatting (3DGS) with differentiable deep reinforcement learning (DDRL) to train vision-based drone navigation policies. By leveraging high-fidelity 3D scene representations and differentiable simulation, our method improves sample efficiency and sim-to-real transfer. Additionally, we incorporate a Context-aided Estimator Network (CENet) to adapt to environmental variations at runtime. Moreover, by curriculum training in a mixture of different surrounding environments, we achieve in-task generalization, the ability to solve new instances of a task not seen during training. Drone hardware experiments demonstrate our method’s high training efficiency compared to state-of-the-art RL methods, zero shot sim-to-real transfer for real robot deployment without fine tuning, and ability to adapt to new instances within the same task class (e. g. to fly through a gate at different locations with different distractors in the environment). Our simulator and training framework are open-sourced at: https://github.com/Qianzhong-Chen/grad_nav.

NeurIPS Conference 2025 Conference Paper

SAS: Simulated Attention Score

Chuanyang Zheng
Jiankai Sun
Yihang Gao
Yuehao Wang
Peihao Wang
Jing Xiong
Liliang Ren
Hao Cheng

The attention mechanism is a core component of the Transformer architecture. Various methods have been developed to compute attention scores, including multi-head attention (MHA), multi-query attention, group-query attention and so on. We further analyze the MHA and observe that its performance improves as the number of attention heads increases, provided the hidden size per head remains sufficiently large. Therefore, increasing both the head count and hidden size per head with minimal parameter overhead can lead to significant performance gains at a low cost. Motivated by this insight, we introduce Simulated Attention Score (SAS), which maintains a compact model size while simulating a larger number of attention heads and hidden feature dimension per head. This is achieved by projecting a low-dimensional head representation into a higher-dimensional space, effectively increasing attention capacity without increasing parameter count. Beyond the head representations, we further extend the simulation approach to feature dimension of the key and query embeddings, enhancing expressiveness by mimicking the behavior of a larger model while preserving the original model size. To control the parameter cost, we also propose Parameter-Efficient Attention Aggregation (PEAA). Comprehensive experiments on a variety of datasets and tasks demonstrate the effectiveness of the proposed SAS method, achieving significant improvements over different attention variants.

IROS Conference 2024 Conference Paper

CLIPSwarm: Generating Drone Shows from Text Prompts with Vision-Language Models

Pablo Pueyo
Eduardo Montijano
Ana C. Murillo
Mac Schwager

This paper introduces CLIPSwarm, a new algorithm designed to automate the modeling of swarm drone formations based on natural language. The algorithm begins by enriching a provided word, to compose a text prompt that serves as input to an iterative approach to find the formation that best matches the provided word. The algorithm iteratively refines formations of robots to align with the textual description, employing different steps for "exploration" and "exploitation". Our framework is currently evaluated on simple formation targets, limited to contour shapes. A formation is visually represented through alpha-shape contours and the most representative color is automatically found for the input word. To measure the similarity between the description and the visual representation of the formation, we use CLIP [1], encoding text and images into vectors and assessing their similarity. Sub-sequently, the algorithm rearranges the formation to visually represent the word more effectively, within the given constraints of available drones. Control actions are then assigned to the drones, ensuring robotic behavior and collision-free movement. Experimental results demonstrate the system’s efficacy in accurately modeling robot formations from natural language descriptions. The algorithm’s versatility is showcased through the execution of drone shows in photorealistic simulation with varying shapes. We refer the reader to the supplementary video for a visual reference of the results.

IROS Conference 2024 Conference Paper

Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting

Aiden Swann
Matthew Strong
Won Kyung Do
Gadiel Sznaier Camps
Mac Schwager
Monroe Kennedy III

In this work, we propose a novel method to supervise 3D Gaussian Splatting (3DGS) scenes using optical tactile sensors. Optical tactile sensors have become widespread in their use in robotics for manipulation and object representation; however, raw optical tactile sensor data is unsuitable to directly supervise a 3DGS scene. Our representation leverages a Gaussian Process Implicit Surface to implicitly represent the object, combining many touches into a unified representation with uncertainty. We merge this model with a monocular depth estimation network, which is aligned in a two stage process, coarsely aligning with a depth camera and then finely adjusting to match our touch data. For every training image, our method produces a corresponding fused depth and uncertainty map. Utilizing this additional information, we propose a new loss function, variance-weighted depth supervised loss, for training the 3DGS scene model. We leverage the DenseTact optical tactile sensor and RealSense RGB-D camera to show that combining touch and vision in this manner leads to quantitatively and qualitatively better results than vision or touch alone in few-view scene synthesis on opaque, reflective and transparent objects. Please see our project page at armlabstanford.github.io/touchgs.

IROS Conference 2023 Conference Paper

CineTransfer: Controlling a Robot to Imitate Cinematographic Style from a Single Example

Pablo Pueyo
Eduardo Montijano
Ana C. Murillo
Mac Schwager

This work presents CineTransfer, an algorithmic framework that drives a robot to record a video sequence that mimics the cinematographic style of an input video. We propose features that abstract the aesthetic style of the input video, so the robot can transfer this style to a scene with visual details that are significantly different from the input video. The framework builds upon CineMPC, a tool that allows users to control cinematographic features, like subjects' position on the image and the depth of field, by manipulating the intrinsics and extrinsics of a cinematographic camera. However, CineMPC requires a human expert to specify the desired style of the shot (composition, camera motion, zoom, focus, etc). CineTransfer bridges this gap, aiming a fully autonomous cinematographic platform. The user chooses a single input video as a style guide. CineTransfer extracts and optimizes two important style features, the composition of the subject in the image and the scene depth of field, and provides instructions for CineMPC to control the robot to record an output sequence that matches these features as closely as possible. In contrast with other style transfer methods, our approach is a lightweight and portable framework which does not require deep network training or extensive datasets. Experiments with real and simulated videos demonstrate the system's ability to analyze and transfer style between recordings, and are available in the supplementary video 1 1 https://youtu.be/_QzNz5WUtpk

NeurIPS Conference 2023 Conference Paper

Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model

Jiankai Sun
Yiqi Jiang
Jianing Qiu
Parth Nobel
Mykel J Kochenderfer
Mac Schwager

Robotic applications often involve working in environments that are uncertain, dynamic, and partially observable. Recently, diffusion models have been proposed for learning trajectory prediction models trained from expert demonstrations, which can be used for planning in robot tasks. Such models have demonstrated a strong ability to overcome challenges such as multi-modal action distributions, high-dimensional output spaces, and training instability. It is crucial to quantify the uncertainty of these dynamics models when using them for planning. In this paper, we quantify the uncertainty of diffusion dynamics models using Conformal Prediction (CP). Given a finite number of exchangeable expert trajectory examples (called the “calibration set”), we use CP to obtain a set in the trajectory space (called the “coverage region”) that is guaranteed to contain the output of the diffusion model with a user-defined probability (called the “coverage level”). In PlanCP, inspired by concepts from conformal prediction, we modify the loss function for training the diffusion model to include a quantile term to encourage more robust performance across the variety of training examples. At test time, we then calibrate PlanCP with a conformal prediction process to obtain coverage sets for the trajectory prediction with guaranteed coverage level. We evaluate our algorithm on various planning tasks and model-based offline reinforcement learning tasks and show that it reduces the uncertainty of the learned trajectory prediction model. As a by-product, our algorithm PlanCP outperforms prior algorithms on existing offline RL benchmarks and challenging continuous planning tasks. Our method can be combined with most model-based planning approaches to produce uncertainty estimates of the closed-loop system.

ICRA Conference 2023 Conference Paper

Fast and Scalable Signal Inference for Active Robotic Source Seeking

Christopher E. Denniston
Oriana Peltzer
Joshua Ott
Sangwoo Moon
Sung-Kyun Kim
Gaurav S. Sukhatme
Mykel J. Kochenderfer
Mac Schwager

In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking robot planner is a model that can produce estimates of the signal at unknown locations with uncertainty quantification. This model allows the robot to plan for future measurements in the environment. Traditionally, this model has been in the form of a Gaussian process, which has difficulty scaling and cannot represent obstacles. We propose a global and local factor graph model for active source seeking, which allows the model to scale to a large number of measurements and represent unknown obstacles in the environment. We combine this model with extensions to a highly scalable planner to form a system for large-scale active source seeking. We demonstrate that our approach outperforms baseline methods in both simulated and real robot experiments.

IROS Conference 2023 Conference Paper

Local Non-Cooperative Games with Principled Player Selection for Scalable Motion Planning

Makram Chahine
Roya Firoozi
Wei Xiao 0003
Mac Schwager
Daniela Rus

Game-theoretic motion planners are a powerful tool for the control of interactive multi-agent robot systems. Indeed, contrary to predict-then-plan paradigms, game-theoretic planners do not ignore the interactive nature of the problem, and simultaneously predict the behaviour of other agents while considering change in one's policy. This, however, comes at the expense of computational complexity, especially as the number of agents considered grows. In fact, planning with more than a handful of agents can quickly become intractable, disqualifying game-theoretic planners as possible candidates for large scale planning. In this paper, we propose a planning algorithm enabling the use of game-theoretic planners in robot systems with a large number of agents. Our planner is based on the reality of locality of information and thus deploys local games with a selected subset of agents in a receding horizon fashion to plan collision avoiding trajectories. We propose five different principled schemes for selecting game participants and compare their collision avoidance performance. We observe that the use of Control Barrier Functions for priority ranking is a potent solution to the player selection problem for motion planning.

ICRA Conference 2022 Conference Paper

CineMPC: Controlling Camera Intrinsics and Extrinsics for Autonomous Cinematography

Pablo Pueyo
Eduardo Montijano
Ana C. Murillo
Mac Schwager

We present CineMPC, an algorithm to autonomously control a UAV-borne video camera in a nonlinear Model Predicted Control (MPC) loop. CineMPC controls both the position and orientation of the camera-the camera extrinsics-as well as the lens focal length, focal distance, and aperture-the camera intrinsics. While some existing solutions autonomously control the position and orientation of the camera, no existing solutions also control the intrinsic parameters, which are essential tools for rich cinematographic expression. The intrinsic parameters control the parts of the scene that are focused or blurred, the viewers' perception of depth in the scene and the position of the targets in the image. CineMPC closes the loop from camera images to UAV trajectory and lens parameters in order to follow the desired relative trajectory and image composition as the targets move through the scene. Experiments using a photo-realistic environment demon-strate the capabilities of the proposed control framework to successfully achieve a full array of cinematographic effects not possible without full camera control.

IROS Conference 2022 Conference Paper

FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget

Oriana Peltzer
Amanda Bouman
Sung-Kyun Kim
Ransalu Senanayake
Joshua Ott
Harrison Delecki
Mamoru Sobue
Mykel J. Kochenderfer

We present a method for autonomous exploration of large-scale unknown environments under mission time con-straints. We start by proposing the Frontloaded Information Gain Orienteering Problem (FIG-OP) - a generalization of the traditional orienteering problem where the assumption of a reliable environmental model no longer holds. The FIG-OP ad-dresses model uncertainty by frontloading expected information gain through the addition of a greedy incentive, effectively expe-diting the moment in which new area is uncovered. In order to reason across multi-kilometer environments, we solve FIG-OP over an information-efficient world representation, constructed through the aggregation of information from a topological and metric map. Our method was extensively tested and field-hardened across various complex environments, ranging from subway systems to mines. In comparative simulations, we observe that the FIG-OP solution exhibits improved coverage efficiency over solutions generated by greedy and traditional orienteering-based approaches (i. e. severe and minimal model uncertainty assumptions, respectively).

ICRA Conference 2022 Conference Paper

Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers

Rohan Chandra
Mingyu Wang 0002
Mac Schwager
Dinesh Manocha

We present a novel approach for risk-aware planning with human agents in multi-agent traffic scenarios. Our approach takes into account the wide range of human driver behaviors on the road, from aggressive maneuvers like speeding and overtaking, to conservative traits like driving slowly and conforming to the right-most lane. In our approach, we learn a mapping from a data-driven human driver behavior model called the CMetric to a driver's entropic risk preference. We then use the derived risk preference within a game-theoretic risk-sensitive planner to model risk-aware interactions among human drivers and an autonomous vehicle in various traffic scenarios. We demonstrate our method in a merging scenario, where our results show that the final trajectories obtained from the risk-aware planner generate desirable emergent behaviors. Particularly, our planner recognizes aggressive human drivers and yields to them while maintaining a greater distance from them. In a user study, participants were able to distinguish between aggressive and conservative simulated drivers based on trajectories generated from our risk-sensitive planner. We also observe that aggressive human driving results in more frequent lane-changing in the planner. Finally, we compare the performance of our modified risk-aware planner with existing methods and show that modeling human driver behavior leads to safer navigation.

ICRA Conference 2021 Conference Paper

Reachable Polyhedral Marching (RPM): A Safety Verification Algorithm for Robotic Systems with Deep Neural Network Components

Joseph A. Vincent
Mac Schwager

We present a method for computing exact reachable sets for deep neural networks with rectified linear unit (ReLU) activation. Our method is well-suited for use in rigorous safety analysis of robotic perception and control systems with deep neural network components. Our algorithm can compute both forward and backward reachable sets for a ReLU network iterated over multiple time steps, as would be found in a perception-action loop in a robotic system. Our algorithm is unique in that it builds the reachable sets by incrementally enumerating polyhedral cells in the input space, rather than iterating layer-by-layer through the network as in other methods. If an unsafe cell is found, our algorithm can return this result without completing the full reachability computation, thus giving an anytime property that accelerates safety verification. In addition, our method requires less memory during execution compared to existing methods where memory can be a limiting factor. We demonstrate our algorithm on safety verification of the ACAS Xu aircraft advisory system. We find unsafe actions many times faster than the fastest existing method and certify no unsafe actions exist in about twice the time of the existing method. We also compute forward and backward reachable sets for a learned model of pendulum dynamics over a 50 time step horizon in 87s on a laptop computer. Algorithm source code: https://github.com/StanfordMSL/Neural-Network-Reach.

IROS Conference 2021 Conference Paper

Reduced State Value Iteration for Multi-Drone Persistent Surveillance with Charging Constraints

Patrick H. Washington
Mac Schwager

This paper presents Reduced State Value Iteration (RSVI), an algorithm to compute policies for Markov Decision Processes (MDPs) that have natural checkpoints, allowing for a solution based on a reduced state space. The algorithm is applied to find policies for multiple drones to persistently surveil an environment subject to charging constraints. RSVI leverages the structure of the true MDP to build an MDP with a smaller state-action space. Monte Carlo simulations are used to estimate transitions between the states in the reduced MDP, which are used in value iteration to compute a policy for the reduced MDP. States in the true MDP are mapped to reduced states. Actions in the reduced space from the policy are then mapped to actions in the full space for execution on the true MDP. Performance of the RSVI policy improves as the state discretization becomes finer, but with increasing computational requirements, thus giving a natural trade-off between computational resources and policy suboptimality. Results of simulated persistent surveillance experiments show that our RSVI policy outperforms a baseline heuristic.

IROS Conference 2021 Conference Paper

TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation

Claire Chen
Preston Culbertson
Marion Lepert
Mac Schwager
Jeannette Bohg

Dexterous manipulation tasks often require contact switching, where fingers make and break contact with the object. We propose a method that plans trajectories for dexterous manipulation tasks involving contact switching using contact-implicit trajectory optimization (CITO) augmented with a high-level discrete contact sequence planner. We first use the high-level planner to find a sequence of finger contact switches given a desired object trajectory. With this contact sequence plan, we impose additional constraints in the CITO problem. We show that our method finds trajectories approximately 7 times faster than a general CITO baseline for a four-finger planar manipulation scenario. Furthermore, when executing the planned trajectories in a full dynamics simulator, we are able to more closely track the object pose trajectories planned by our method than those planned by the baselines.

IROS Conference 2020 Conference Paper

CinemAirSim: A Camera-Realistic Robotics Simulator for Cinematographic Purposes

Pablo Pueyo
Eric Cristofalo
Eduardo Montijano
Mac Schwager

Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular in the film and entertainment industries, in part because of their maneuverability and perspectives they enable. While there exists methods for controlling the position and orientation of the drones for visibility, other artistic elements of the filming process, such as focal blur, remain unexplored in the robotics community. The lack of cinematographic robotics solutions is partly due to the cost associated with the cameras and devices used in the filming industry, but also because state-of-the-art photo-realistic robotics simulators only utilize a full in-focus pinhole camera model which does not incorporate these desired artistic attributes. To overcome this, the main contribution of this work is to endow the well-known drone simulator, AirSim, with a cinematic camera as well as extend its API to control all of its parameters in real time, including various filming lenses and common cinematographic properties. In this paper, we detail the implementation of our AirSim modification, CinemAirSim, present examples that illustrate the potential of the new tool, and highlight the new research opportunities that the use of cinematic cameras can bring to research in robotics and control.

IROS Conference 2020 Conference Paper

Distributed Motion Control for Multiple Connected Surface Vessels

Wei Wang 0078
Zijian Wang 0003
Luis A. Mateos
Kuan Wei Huang
Mac Schwager
Carlo Ratti
Daniela Rus

We propose a scalable cooperative control approach which coordinates a group of rigidly connected autonomous surface vessels to track desired trajectories in a planar water environment as a single floating modular structure. Our approach leverages the implicit information of the structure’s motion for force and torque allocation without explicit communication among the robots. In our system, a leader robot steers the entire group by adjusting its force and torque according to the structure’s deviation from the desired trajectory, while follower robots run distributed consensus-based controllers to match their inputs to amplify the leader’s intent using only onboard sensors as feedback. To cope with the nonlinear system dynamics in the water, the leader robot employs a nonlinear model predictive controller (NMPC), where we experimentally estimated the dynamics model of the floating modular structure in order to achieve superior performance for leader-following control. Our method has a wide range of potential applications in transporting humans and goods in many of today’s existing waterways. We conducted trajectory and orientation tracking experiments in hardware with three custom-built autonomous modular robotic boats, called Roboat, which are capable of holonomic motions and onboard state estimation. Simulation results with up to 65 robots also prove the scalability of our proposed approach.

ICRA Conference 2020 Conference Paper

Distributed Multi-Target Tracking for Autonomous Vehicle Fleets

Ola Shorinwa
Javier Yu
Trevor Halsted
Alex Koufos
Mac Schwager

We present a scalable distributed target tracking algorithm based on the alternating direction method of multipliers that is well-suited for a fleet of autonomous cars communicating over a vehicle-to-vehicle network. Each sensing vehicle communicates with its neighbors to execute iterations of a Kalman filter-like update such that each agent's estimate approximates the centralized maximum a posteriori estimate without requiring the communication of measurements. We show that our method outperforms the Consensus Kalman Filter in recovering the centralized estimate given a fixed communication bandwidth. We also demonstrate the algorithm in a high fidelity urban driving simulator (CARLA), in which 50 autonomous cars connected on a time-varying communication network track the positions and velocities of 50 target vehicles using on-board cameras.

ICRA Conference 2020 Conference Paper

Enhancing Game-Theoretic Autonomous Car Racing Using Control Barrier Functions

Gennaro Notomista
Mingyu Wang 0002
Mac Schwager
Magnus Egerstedt

In this paper, we consider a two-player racing game, where an autonomous ego vehicle has to be controlled to race against an opponent vehicle, which is either autonomous or human-driven. The approach to control the ego vehicle is based on a Sensitivity-ENhanced NAsh equilibrium seeking (SENNA) method, which uses an iterated best response algorithm in order to optimize for a trajectory in a two-car racing game. This method exploits the interactions between the ego and the opponent vehicle that take place through a collision avoidance constraint. This game-theoretic control method hinges on the ego vehicle having an accurate model and correct knowledge of the state of the opponent vehicle. However, when an accurate model for the opponent vehicle is not available, or the estimation of its state is corrupted by noise, the performance of the approach might be compromised. For this reason, we augment the SENNA algorithm by enforcing Permissive RObust SafeTy (PROST) conditions using control barrier functions. The objective is to successfully overtake or to remain in the front of the opponent vehicle, even when the information about the latter is not fully available. The successful synergy between SENNA and PROST-antithetical to the notable rivalry between the two namesake Formula 1 drivers-is demonstrated through extensive simulated experiments.

IROS Conference 2020 Conference Paper

Game-Theoretic Planning for Risk-Aware Interactive Agents

Mingyu Wang 0002
Negar Mehr
Adrien Gaidon
Mac Schwager

Modeling the stochastic behavior of interacting agents is key for safe motion planning. In this paper, we study the interaction of risk-aware agents in a game-theoretical framework. Under the entropic risk measure, we derive an iterative algorithm for approximating the intractable feedback Nash equilibria of a risk-sensitive dynamic game. We use an iteratively linearized approximation of the system dynamics and a quadratic approximation of the cost function in solving a backward recursion for finding feedback Nash equilibria. In this respect, the algorithm shares a similar structure with DDP and iLQR methods. We conduct experiments in a set of challenging scenarios such as roundabouts. Compared to ignoring the game interaction or the risk sensitivity, we show that our risk-sensitive game-theoretic framework leads to more timeefficient, intuitive, and safe behaviors when facing underlying risks and uncertainty.

ICRA Conference 2020 Conference Paper

Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning

Kyle Brown
Oriana Peltzer
Martin A. Sehr
Mac Schwager
Mykel J. Kochenderfer

We study the problem of sequential task assignment and collision-free routing for large teams of robots in applications with inter-task precedence constraints (e. g. , task A and task B must both be completed before task C may begin). Such problems commonly occur in assembly planning for robotic manufacturing applications, in which sub-assemblies must be completed before they can be combined to form the final product. We propose a hierarchical algorithm for computing makespan-optimal solutions to the problem. The algorithm is evaluated on a set of randomly generated problem instances where robots must transport objects between stations in a "factory" grid world environment. In addition, we demonstrate in high-fidelity simulation that the output of our algorithm can be used to generate collision-free trajectories for non-holonomic differential-drive robots.

IROS Conference 2020 Conference Paper

Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction

Haruki Nishimura
Boris Ivanovic
Adrien Gaidon
Marco Pavone 0001
Mac Schwager

This paper presents a novel online framework for safe crowd-robot interaction based on risk-sensitive stochastic optimal control, wherein the risk is modeled by the entropic risk measure. The sampling-based model predictive control relies on mode insertion gradient optimization for this risk measure as well as Trajectron++, a state-of-the-art generative model that produces multimodal probabilistic trajectory forecasts for multiple interacting agents. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control, which is advantageous compared to end-to-end policy learning methods in that it allows the robot's desired behavior to be specified at run time. In particular, we show that the robot exhibits diverse interaction behavior by varying the risk sensitivity parameter. A simulation study and a real-world experiment show that the proposed online framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.

IROS Conference 2020 Conference Paper

Scalable Collaborative Manipulation with Distributed Trajectory Planning

Ola Shorinwa
Mac Schwager

We present a distributed algorithm to enable a group of robots to collaboratively manipulate an object to a desired configuration while avoiding obstacles. Each robot solves a local optimization problem iteratively and communicates with its local neighbors, ultimately converging to the optimal trajectory of the object over a receding horizon. The algorithm scales efficiently to large groups, with a convergence rate constant in the number of robots, and can enforce constraints that are only known to a subset of the robots, such as for collision avoidance using local online sensing. We show that the algorithm converges many orders of magnitude faster, and results in a tracking error two orders of magnitude lower, than competing distributed collaborative manipulation algorithms based on Consensus alternating direction method of multipliers (ADMM).

IROS Conference 2019 Conference Paper

Multi-Robot Assembly Sequencing via Discrete Optimization

Preston Culbertson
Saptarshi Bandyopadhyay
Mac Schwager

Multi-robot assembly has the potential to greatly reduce the cost and risk associated with the fabrication of large structures. Using teams of robots to perform assembly offers numerous advantages such as parallelism, robustness to single-agent failures, and flexibility in scheduling and task assignment. However, while previous work on multi-robot assembly focuses on generating feasible assembly plans and decentralized control strategies, we instead study the problem of planning optimal assembly sequences. To this end, we pose the problem of multi-robot assembly as a discrete optimization, specifically an integer linear program (ILP) or quadratic program (IQP), which aims to minimize the time to complete the assembly, or to minimize the distance traveled. We develop a model of multi-robot assembly that captures both geometric constraints and actuation constraints inherent to the problem. While the ILP and IQP can be solved exactly using commercial optimization software in a substantial amount of time, we also propose heuristic strategies which can be quickly computed, and can scale to structures of reasonable size. We also verify our methods empirically by comparing their performance on a variety of test structures.

IROS Conference 2019 Conference Paper

Trust But Verify: A Distributed Algorithm for Multi-Robot Wireframe Exploration and Mapping

Adam Caccavale
Mac Schwager

This paper presents a novel distributed mapping algorithm for multiple resource-constrained robots operating in a rectilinear 2D environment. The algorithm is built upon the sparse wireframe map representation and updating framework in [1]. We propose an exploration strategy based on the labeling of the vertices in the wireframe map, combined with a map-merging interrupt routine that is activated when robots enter into communication range with one another. The maps are not naively merged, but instead the receiving robot verifies the received information before it is assimilated by attempting to drive to the location where the other robot was when communication was established. The robots do not share a global coordinate frame, so prior to a merge the relative map alignment is determined. This is achieved using the random sample consensus (RANSAC) framework with a custom feature which leverages the structure inherent in the wireframe map representation. This results in a lower rate of false-positive matches compared to another state-of-the-art feature used in point cloud alignment, the 4-point congruent set (4PCS). We show our feature to be more robust to false-positive alignments, a common occurrence when attempting to align sparse structures such as wireframe maps. We present high fidelity simulation results in a ROS-Gazebo environment with lidar-equipped TurtleBots 1 to highlight the benefits of our algorithm. 1 The TurtleBot3 burger configuration www. turtlebot.com.

ICRA Conference 2018 Conference Paper

Active Motion-Based Communication for Robots with Monocular Vision

Haruki Nishimura
Mac Schwager

In this paper, we consider motion as a means of sending messages between robots. We focus on a scenario in which a message is encoded in a sending robot's trajectory, and decoded by a receiver robot equipped with a monocular camera. The relative pose between the robots is unknown. We introduce an online Bayesian estimation algorithm based on the Multi-hypothesis Extended Kalman Filter for the receiving robot to simultaneously estimate its relative pose to the sender, and the trajectory class of the sender. The difficulty in this problem arises from the monocular vision model of the receiver and the unknown relative pose between robots, which brings inherent ambiguity into the trajectory identification, and hence the message decoding. An active vision-based control policy is derived and combined with the Bayesian estimation in order to deal with this difficulty. The policy is constructed online based on Monte Carlo Tree Search and aims at reducing the entropy over the trajectory class distribution. The algorithm has broad applications, e. g. , to intent modeling and motion prediction for autonomous driving and autonomous drone operations. Simulation results demonstrate that the proposed estimation algorithm and the control policy result in an accurate trajectory classification.

ICRA Conference 2018 Conference Paper

Cooperative Object Transport in 3D with Multiple Quadrotors Using No Peer Communication

Zijian Wang 0003
Sumeet Singh
Marco Pavone 0001
Mac Schwager

We present a framework to enable a fleet of rigidly attached quadrotor aerial robots to transport heavy objects along a known reference trajectory without inter-robot communication or centralized coordination. Leveraging a distributed wrench controller, we provide exponential stability guarantees for the entire assembly, under a mild geometric condition. This is achieved by each quadrotor independently solving a local optimization problem to counteract the biased torque effects from each robot in the assembly. We rigorously analyze the controllability of the object, design a distributed compensation scheme to address these challenges, and show that the resulting strategy collectively guarantees full group control authority. To ensure feasibility for online implementation, we derive bounds on the net desired control wrench, characterize the output wrench space of each quadrotor, and perform subsequent trajectory optimization under these input constraints. We thoroughly validate our method in simulation with eight quadrotors transporting a heavy object in a cluttered environment subject to various sources of uncertainty, and demonstrate the algorithm's resilience.

ICRA Conference 2018 Conference Paper

Decentralized Adaptive Control for Collaborative Manipulation

Preston Culbertson
Mac Schwager

This paper presents a design for a decentralized adaptive controller that allows a team of agents to manipulate a common payload in $\mathbb{R}^{2}$ or $\mathbb{R}^{3}$. The controller requires no communication between agents and requires no a priori knowledge of agent positions or payload properties. The agents can control the payload to track a reference trajectory in linear and angular velocity with center-of-mass measurements, in angular velocity using only local measurements and a common frame, and can stabilize its rotation with only local measurements. The controller is designed via a Lyapunov-style analysis and has proven stability and convergence. The controller is validated in simulation and experimentally with four robots manipulating an object in the plane.

IROS Conference 2018 Conference Paper

Distributed Deep Reinforcement Learning for Fighting Forest Fires with a Network of Aerial Robots

Ravi N. Haksar
Mac Schwager

This paper proposes a distributed deep reinforcement learning (RL) based strategy for a team of Unmanned Aerial Vehicles (UAVs) to autonomously fight forest fires. We first model the forest fire as a Markov decision process (MDP) with a factored structure. We consider optimally controlling the forest fire without agents using dynamic programming, and show any exact solution and many approximate solutions are computationally intractable. Given the problem complexity, we consider a deep RL approach in which each agent learns a policy requiring only local information. We show with Monte Carlo simulations that the deep RL policy outperforms a hand-tuned heuristic, and scales well for various forest sizes and different numbers of UAVs as well as variations in model parameters. Experimental demonstrations with mobile robots fighting a simulated forest fire in the Robotarium at the Georgia Institute of Technology are also presented.

ICRA Conference 2018 Conference Paper

Safe Distributed Lane Change Maneuvers for Multiple Autonomous Vehicles Using Buffered Input Cells

Mingyu Wang 0002
Zijian Wang 0003
Shreyasha Paudel
Mac Schwager

This paper introduces the Buffered Input Cell as a reciprocal collision avoidance method for multiple vehicles with high-order linear dynamics, extending recently proposed methods based on the Buffered Voronoi Cell [1] and generalized Voronoi diagrams [2]. We prove that if each vehicle's control input remains in its Buffered Input Cell at each time step, collisions will be avoided indefinitely. The method is fast, reactive, and only requires that each vehicle measures the relative position of neighboring vehicles. We incorporate this collision avoidance method as one layer of a complete lane change control stack for autonomous cars in a freeway driving scenario. The lane change control stack comprises a decision-making layer, a trajectory planning layer, a trajectory following feedback controller, and the Buffered Input Cell for collision avoidance. We show in simulations that collisions are avoided with multiple vehicles simultaneously changing lanes on a freeway. We also show in simulations that autonomous cars using the BIC method effectively avoid collisions with an aggressive human-driven car.

IROS Conference 2018 Conference Paper

Wireframe Mapping for Resource-Constrained Robots*This research was supported in part by NSF grant CMMI-1562335 and ONR grant N00014-12-1-1000. We are grateful for this support

Adam Caccavale
Mac Schwager

This paper presents a novel wireframe map structure for resource-constrained robots operating in a rectilinear 2D environment. The wireframe representation compactly represents geometry, in addition to transient situations such as occlusions and boundaries of unexplored regions. We formulate a particle filter to suit this sparse wireframe map structure. Functions for calculating the likelihood of scans, merging wireframes, and resampling are developed to accommodate this map representation. The wireframe structure with the particle filter allows for severe discrete map errors to be corrected, leading to accurate maps with small storage requirements. We show in a simulation study that the algorithm attains a map of an environment with 1 % error, compared to an occupancy grid map obtained with GMapping which attained 23% error with the same storage requirements. A simulation mapping a large environment demonstrates the algorithms scalability.

ICRA Conference 2017 Conference Paper

A distributed algorithm for mapping the graphical structure of complex environments with a swarm of robots

Adam Caccavale
Mac Schwager

This paper presents a novel multi-robot mapping algorithm which allows a large number of simple robots to map the discrete graphical structure underlying an environment of multiple disjoint subregions. Examples of such environments include rooms in a building, buildings in a town, chambers in a cave network, or islands in an archipelago. Each robot is limited to a small communication range, compass, GPS sensor, and a short-range proximity sensor (e. g. bump sensor). Furthermore memory is limited, so no metric map of the environment is stored. Instead the algorithm determines which robots inhabit the same subregion, and which of these groups of robots are able to communicate. This information is captured in a disk graph representation. It is proven that these simple capabilities are sufficient to guarantee that all agents will determine the graphical structure in a finite time. Two environment configurations were tested with a range of quantities of robots. These simulations confirm that processing time is polynomial in the number of robots and indicate that the number of steps to convergence is linear in the number of robots.

IROS Conference 2017 Conference Paper

Linear actuator robots: Differential kinematics, controllability, and algorithms for locomotion and shape morphing

Nathan S. Usevitch
Zachary M. Hammond
Sean Follmer
Mac Schwager

We consider a class of robotic systems composed of high elongation linear actuators connected at universal joints. We derive the differential kinematics of such robots, and formalize concepts of controllability based on graph rigidity. Control methods are then developed for two separate applications: locomotion and shape morphing. The control algorithm in both cases solves a series of linearly constrained quadratic programs at each time step to minimize an objective function while ensuring physical feasibility. We present simulation results for locomotion along a prescribed path, and morphing to a target shape.

ICRA Conference 2016 Conference Paper

Assistive collision avoidance for quadrotor swarm teleoperation

Dingjiang Zhou
Mac Schwager

This paper presents a method for controlling quadrotor swarms in an environment with obstacles, with an intuitive human-swarm interface operated by a single human user. Our method allows for the quadrotor swarm to maintain a desired formation, while also keeping the quadrotors a safe distance from obstacles and from one another. We use a Virtual Rigid Body abstraction to provide a bridge between the single human user and the quadrotor swarm, so that a human user can fly an arbitrarily large quadrotor swarm from a single joystick. By applying multiple vector fields, collisions are automatically avoided within the swarm of quadrotors, and between the quadrotors and obstacles, while the Virtual Rigid Body is controlled by the human user. Our method is demonstrated in hardware experiments with groups of quadrotor micro aerial vehicles teleoperated by a single human operator in a motion capture system.

ICRA Conference 2016 Conference Paper

Cooperative multi-quadrotor pursuit of an evader in an environment with no-fly zones

Alyssa Pierson
Armin Ataei
Ioannis Ch. Paschalidis
Mac Schwager

We investigate the cooperative pursuit of an evader by a group of quadrotors in an environment with no-fly zones. While the pursuers cannot enter into no-fly zones, the evader may freely move through zones to avoid capture. Once the evader enters a no-fly zone, the pursuers calculate a reachable set of evader positions. Using tools from Voronoi-based coverage control applied to the evader's reachable set, we provide an algorithm that distributes the pursuers around the zone's boundary and minimizes the capture time once the evader leaves the no-fly zone. Robust model predictive control (RMPC) tools are used to control the quadrotors and to ensure that they always remain in free space. We demonstrate the performance of our proposed algorithms through a series of experiments on KMEL Nano+ quadrotors.

ICRA Conference 2016 Conference Paper

Distributed formation control of non-holonomic robots without a global reference frame

Eduardo Montijano
Eric Cristofalo
Mac Schwager
Carlos Sagüés

In this paper we consider the problem of controlling a team of non-holonomic robots to reach a desired formation. The formation is described in terms of the desired relative positions and orientations the robots need to keep with respect to each other, and it is assumed that the robots do not have a common shared reference frame. In other words, the robots can use only on-board sensing to achieve the formation. We first consider a holonomic framework, using a well known distance-based approach to reach a formation for the positions. We then include a control law for the orientations. We further discuss the problem of mirror configurations that appear when different desired relative orientations can satisfy the same distance-based constraints through different formations. Exploiting the concept of chirality, we present a relabeling strategy to reassign the robots' roles to reach the desired pattern when a mirror configuration occurs. The distance-based holonomic control is then transformed to cope with the non-holonomic constraints using a piecewise-smooth function. Simulation results, as well as hardware experiments with five m3pi robots demonstrate the applicability of our approach.

ICRA Conference 2016 Conference Paper

Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus

Javier Alonso-Mora
Eduardo Montijano
Mac Schwager
Daniela Rus

This paper presents a distributed method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The robots are assumed to have a reduced communication and visibility radius and share information with their neighbors. Via distributed consensus the robots compute (a) the convex hull of the robot positions and (b) the largest convex region within free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation within this convex neighborhood of the robots. Reconfiguration is allowed, when required, by considering a set of target formations. The robots navigate towards the target collision-free formation with individual local planners that account for their dynamics. The approach is efficient and scalable with the number of robots and performs well in simulations with up to sixteen quadrotors.

ICRA Conference 2016 Conference Paper

Kinematic multi-robot manipulation with no communication using force feedback

Zijian Wang 0003
Mac Schwager

This paper proposes a novel decentralized algorithm that coordinates the forces of a group of robots during a cooperative manipulation task. The highlight of our approach is that no communication is needed between any two robots. Our underlying intuition is that every follower robot can measure the direction of the movement of the object and then applies its force along that direction to reinforce the movement. We prove that using our algorithm, all followers' forces will synchronize to the direction of the force applied by one leader robot, who guides the robotic fleet to its destination. We first verify our algorithm by simulation in a physics engine, where 20 robots transport a chair collectively. We then validate our algorithm in hardware experiments by building four low-cost robots, equipped with force and velocity sensors, to transport a cardboard box in a laboratory environment. In addition, our algorithm allows the leader to be a human, and we also demonstrate the human-swarm cooperation in our manipulation experiments.

ICRA Conference 2015 Conference Paper

Adapting to performance variations in multi-robot coverage

Alyssa Pierson
Lucas Coelho Figueiredo
Luciano C. A. Pimenta
Mac Schwager

This paper proposes a new approach for a group of robots carrying out a collaborative task to adapt on-line to actuation performance variations among the robots. We consider the problem of multi-robot coverage, where a group of robots has to spread out to cover the environment. We suppose that some robots have poor actuation performance (e. g. weak motors, friction losses in the gear train, wheel slip, etc.) and some have strong actuation performance (powerful motors, little friction, favorable terrain, etc.). The robots do not know before hand the relative strengths of their actuation compared to the others in the team. The algorithm in this paper learns the relative actuation performance variations among the robots on-line, in a distributed fashion, and automatically compensates by giving the weak robots a small portion of the environment, and giving the strong robots a larger portion. Using a Lyapunov-type proof, we prove that the robots converge to locally optimal positions for coverage. The algorithm is demonstrated in both Matlab simulations and experiments using Pololu m3pi robots.

ICRA Conference 2015 Conference Paper

Bio-inspired non-cooperative multi-robot herding

Alyssa Pierson
Mac Schwager

This paper presents a new control strategy to control a group of dog-like robots to drive a herd of non-cooperative sheep-like agents to a goal region in the environment. The sheep-like agents, which may be biological or robotic, respond to the presence of the dog-like robots with a repelling potential field common in biological models of the behavior of herding animals. Our key insight in designing control laws for the dog-like robots is to enforce geometrical relationships that allow for the combined dynamics of the dogs and sheep to be mapped to a simple unicycle robot model. We prove convergence of a single sheep to a desired goal region using two or more dogs, and we propose a control strategy for the case of any number of sheep driven by two or more dogs. Simulations in Matlab and hardware experiments with Pololu m3pi robots demonstrate the effectiveness of our control strategy.

ICRA Conference 2015 Conference Paper

Virtual Rigid Bodies for coordinated agile maneuvering of teams of micro aerial vehicles

Dingjiang Zhou
Mac Schwager

This paper proposes a method for controlling a team of quadrotor micro aerial vehicles to perform agile maneuvers while holding a fixed relative formation, as well as transitioning between a sequence of formations. The objective is to coordinate the quadrotors to fly in intricate interlaced patterns, similarly to an air show demonstration team. The paper proposes a new abstraction, called a Virtual Rigid Body, which allows the quadrotors to hold relative positions while executing agile maneuvers as a group. By planning trajectories for the Virtual Rigid Body in SE(3), trajectories for each quadrotor are obtained in order to maintain the desired formation during the maneuver. The paper also proposes a method for sequencing a series of Virtual Rigid Body formations, and automatically designing collision free transitions between successive formations, while the team simultaneously executes a trajectory in SE(3). The resulting sequence of formations and transitions gives trajectories that weave intricate designs while avoiding collisions. The method is demonstrated experimentally with three KMel K500 quadrotors flying in a motion capture environment.

IROS Conference 2014 Conference Paper

Correlated Orienteering Problem and its application to informative path planning for persistent monitoring tasks

Jingjin Yu
Mac Schwager
Daniela Rus

We propose a novel non-linear extension to the Orienteering Problem (OP), called the Correlated Orienteering Problem (COP). We use COP to plan informative tours (cyclic paths) for persistent monitoring of an environment with spatial correlations, where the tours are constrained to a fixed length or time budget. The main feature of COP is a quadratic utility function that captures spatial correlations among points of interest that are close to each other. COP may be solved using mixed integer quadratic programming (MIQP) that can plan multiple disjoint tours that maximize the quadratic utility function. We perform extensive characterization of our method to verify its correctness, as well as its applicability to the estimation of a realistic, time-varying, and spatially correlated scalar field.

ICRA Conference 2014 Conference Paper

Vector field following for quadrotors using differential flatness

Dingjiang Zhou
Mac Schwager

This paper proposes a differential flatness-based method for maneuvering a quadrotor so that its position follows a specified velocity vector field. Existing planning and control algorithms often give a 2D or 3D velocity vector field to be followed by a robot. However, quadrotors have complex nonlinear dynamics that make vector field following difficult, especially in aggressive maneuvering regimes. This paper exploits the differential flatness property of a quadrotor's dynamics to control its position along a given vector field. Differential flatness allows for the analytical derivation of control inputs in order to control the 12D dynamical state of the quadrotor such that the 2D or 3D position of the quadrotor follows the flow specified by a given vector field. The method is derived mathematically, and demonstrated in numerical simulations and in experiments with a quadrotor robot for three different vector fields.

ICRA Conference 2013 Conference Paper

A receding horizon algorithm for informative path planning with temporal logic constraints

Austin Jones
Mac Schwager
Calin Belta

This paper considers the problem of finding the most informative path for a sensing robot under temporal logic constraints, a richer set of constraints than have previously been considered in information gathering. An algorithm for informative path planning is presented that leverages tools from information theory and formal control synthesis, and is proven to give a path that satisfies the given temporal logic constraints. The algorithm uses a receding horizon approach in order to provide a reactive, on-line solution while mitigating computational complexity. Statistics compiled from multiple simulation studies indicate that this algorithm performs better than a baseline exhaustive search approach.

ICRA Conference 2013 Conference Paper

Planning periodic persistent monitoring trajectories for sensing robots in Gaussian Random Fields

Xiaodong Lan
Mac Schwager

This paper considers the problem of planning a trajectory for a sensing robot to best estimate a time-changing Gaussian Random Field in its environment. The robot uses a Kalman filter to maintain an estimate of the field value, and to compute the error covariance matrix of the estimate. A new randomized path planning algorithm is proposed to find a periodic trajectory for the sensing robot that tries to minimize the largest eigenvalue of the error covariance matrix over an infinite horizon. The algorithm is proven to find the minimum infinite horizon cost cycle in a graph, which grows by successively adding random points. The algorithm leverages recently developed methods for periodic Riccati recursions to efficiently compute the infinite horizon cost of the cycles, and it uses the monotonicity property of the Riccati recursion to efficiently compare the cost of different cycles without explicitly computing their costs. The performance of the algorithm is demonstrated in numerical simulations.

IROS Conference 2012 Conference Paper

Generating informative paths for persistent sensing in unknown environments

Daniel E. Soltero
Mac Schwager
Daniela Rus

We present an online algorithm for a robot to shape its path to a locally optimal configuration for collecting information in an unknown dynamic environment. As the robot travels along its path, it identifies both where the environment is changing, and how fast it is changing. The algorithm then morphs the robot's path online to concentrate on the dynamic areas in the environment in proportion to their rate of change. A Lyapunov-like stability proof is used to show that, under our proposed path shaping algorithm, the path converges to a locally optimal configuration according to a Voronoi-based coverage criterion. The path shaping algorithm is then combined with a previously introduced speed controller to produce guaranteed persistent monitoring trajectories for a robot in an unknown dynamic environment. Simulation and experimental results with a quadrotor robot support the proposed approach.

IROS Conference 2011 Conference Paper

A scalable information theoretic approach to distributed robot coordination

Brian J. Julian
Michael Angermann
Mac Schwager
Daniela Rus

This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of the estimate by following the gradient of mutual information. Both the filter and the controller use a novel algorithm for approximating the robots' joint measurement probabilities, which combines consensus (for decentralization) and sampling (for scalability). The approximations are shown to approach the true joint measurement probabilities as the size of the consensus rounds grows or as the network becomes complete. The resulting gradient controller runs in constant time with respect to the number of robots, and linear time with respect to the number of sensor measurements and environment discretization cells, while traditional mutual information methods are exponential in all of these quantities. Furthermore, the controller is proven to be convergent between consensus rounds and, under certain conditions, is locally optimal. The complete distributed inference and coordination algorithm is demonstrated in experiments with five quad-rotor flying robots and simulations with 100 robots.

ICRA Conference 2011 Conference Paper

Persistent monitoring of changing environments using a robot with limited range sensing

Stephen L. Smith 0001
Mac Schwager
Daniela Rus

This paper presents controllers that enable a mobile robot to persistently monitor or sweep a changing environment. The changing environment is modeled as an accumulation function which grows in areas that are not within range of the robot, and decreases in areas that are within range of the robot. The robot must continually move through the environment to prevent the accumulation of any area from growing unbounded. We consider the case in which a predefined path is given for the robot, and we focus on controlling the robot's speed along the path. We characterize necessary and sufficient conditions on the speed controller of the robot for keeping the accumulation function bounded. We then search among the space of speed controllers that are parametrized by a finite set of basis functions. We develop a linear program to compute the optimal speed controller; that which minimizes the accumulation over the environment. Simulation results illustrate the performance of the controllers.

ICRA Conference 2011 Conference Paper

Persistent ocean monitoring with underwater gliders: Towards accurate reconstruction of dynamic ocean processes

Ryan N. Smith
Mac Schwager
Stephen L. Smith 0001
Daniela Rus
Gaurav S. Sukhatme

This paper proposes a path planning algorithm and a velocity control algorithm for underwater gliders to persistently monitor a patch of ocean. The algorithms address a pressing need among ocean scientists to collect high-value data for studying ocean events of scientific and environmental interest, such as the occurrence of harmful algal blooms. The path planner optimizes a cost function that blends two competing factors: it maximizes the information value of the path, while minimizing the deviation from the path due to ocean currents. The speed control algorithm then optimizes the speed along the planned path so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California over the course of several weeks. The results show significant improvements in data resolution and path reliability compared to a sampling path that is typically used in the region.

ICRA Conference 2011 Conference Paper

Time scales and stability in networked multi-robot systems

Mac Schwager
Nathan Michael
Vijay Kumar 0001
Daniela Rus

This paper examines the dynamic interplay between decentralized controllers and mesh networking protocols for controlling groups of robots. A proportional controller is used to maintain robots in a formation based on estimates of the robots' states observed through the network. The state information is propagated through the network using a flooding algorithm, which introduces topology-dependent time delays. The coupled interaction of information flow over the network with the dynamics of the robots is modeled as a linear dynamical system. With this model it is shown that systems made up of robots with stable first order dynamics are stable for all network update times, positive feedback gains, and connected communication graphs. With higher order robot dynamics it is found that stability is a complex and counter intuitive function of feedback gain and network update time. A performance metric is proposed for analyzing the convergence rate of the multi-robot system. Experiments with flying quadrotor robots verify the predictions of the model and the performance metric.

ICRA Conference 2010 Conference Paper

Optimizing communication in air-ground robot networks using decentralized control

Stephanie Gil
Mac Schwager
Brian J. Julian
Daniela Rus

We develop a distributed controller to position a team of aerial vehicles in a configuration that optimizes communication-link quality, to support a team of ground vehicles performing a collaborative task. We propose a gradient-based control approach where agents' positions locally minimize a physically motivated cost function. The contributions of this paper are threefold. We formulate of a cost function that incorporates a continuous, physical model of signal quality, SIR. We develop a non-smooth gradient-based controller that positions aerial vehicles to acheive optimized signal quality amongst all vehicles in the system. This controller is provably convergent while allowing for non-differentiability due to agents moving in or out of communication with one another. Lastly, we guarantee that given certain initial conditions or certain values of the control parameters, aerial vehicles will never disconnect the connectivity graph. We demonstrate our controller on hardware experiments using AscTec Hummingbird quadrotors and provide aggregate results over 10 trials. We also provide hardware-in-the-loop and MATALB simulation results, which demonstrate positioning of the aerial vehicles to minimize the cost function H and improve signal-quality amongst all communication links in the ground/air robot team.

ICRA Conference 2010 Conference Paper

Voronoi coverage of non-convex environments with a group of networked robots

Andreas Breitenmoser
Mac Schwager
Jean-Claude Metzger
Roland Siegwart
Daniela Rus

This paper presents a solution to decentralized Voronoi coverage in non-convex polygonal environments. We show that complications arise when existing approaches to Voronoi coverage are applied for deploying a group of robots in non-convex environments. We present an algorithm that is guaranteed to converge to a local optimum. Our algorithm combines classical Voronoi coverage with the Lloyd algorithm and the local path planning algorithm TangentBug to compute the motion of the robots around obstacles and corners. We present the algorithm and prove convergence and optimality. We also discuss experimental results from an implementation with five robots.

ICRA Conference 2009 Conference Paper

Optimal coverage for multiple hovering robots with downward facing cameras

Mac Schwager
Brian J. Julian
Daniela Rus

This paper presents a distributed control strategy for deploying hovering robots with multiple downward facing cameras to collectively monitor an environment. Information per pixel is proposed as an optimization criterion for multi-camera placement problems. This metric is used to derive a specific cost function for multiple downward facing cameras mounted on hovering robot platforms. The cost function leads to a gradient-based distributed controller for positioning the robots. A convergence proof using LaSalle's invariance principle is given to show that the robots converge to locally optimal positions. The controller is demonstrated in experiments with three flying quad-rotor robots.

ICRA Conference 2008 Conference Paper

A ladybug exploration strategy for distributed adaptive coverage control

Mac Schwager
Francesco Bullo
David Skelly
Daniela Rus

A control strategy inspired by the hunting tactics of ladybugs is presented to simultaneously achieve sensor coverage and exploration of an area with a group of networked robots. The controller is distributed in that it requires only information local to each robot, and adaptive in that it modifies its behavior based on information in the environment. The ladybug controller is developed as a modification to a basic coverage control law, first for the non-adaptive case, then for the adaptive case. Stability is proven for both cases with a Lyapunov-type proof. Results of numerical simulations are presented.

ICRA Conference 2008 Conference Paper

Consensus learning for distributed coverage control

Mac Schwager
Jean-Jacques E. Slotine
Daniela Rus

A decentralized controller is presented that causes a network of robots to converge to a near optimal sensing configuration, while simultaneously learning the distribution of sensory information in the environment. A consensus (or flocking) term is introduced in the learning law to allow sharing of parameters among neighbors, greatly increasing learning convergence rates. Convergence and consensus is proven using a Lyapunov-type proof. The controller with parameter consensus is shown to perform better than the basic controller in numerical simulations.

ICRA Conference 2007 Conference Paper

Decentralized, Adaptive Control for Coverage with Networked Robots

Mac Schwager
Jean-Jacques E. Slotine
Daniela Rus

A decentralized, adaptive control law is presented to drive a network of mobile robots to a near-optimal sensing configuration. The control law is adaptive in that it integrates sensor measurements to provide a converging estimate of the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. A Lyapunov-type proof is used to show that the control law causes the network to converge to a near-optimal sensing configuration, and the controller is demonstrated in numerical simulations. This technique suggests a broader application of adaptive control methodologies to decentralized control problems in unknown dynamical environments.