Author name cluster

Wenhao Luo

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30 papers

2 author rows

ICRA Conference 2025 Conference Paper

Adaptive Deadlock Avoidance for Decentralized Multi-Agent Systems via CBF-Inspired Risk Measurement

Yanze Zhang
Yiwei Lyu 0002
Siwon Jo
Yupeng Yang
Wenhao Luo

Decentralized safe control plays an important role in multi-agent systems given the scalability and robustness without reliance on a central authority. However, without an explicit global coordinator, the decentralized control methods are often prone to deadlock - a state where the system reaches equilibrium, causing the robots to stall. In this paper, we propose a generalized decentralized framework that unifies the Control Lyapunov Function (CLF) and Control Barrier Function (CBF) to facilitate efficient task execution and ensure deadlock-free trajectories for the multi-agent systems. As the agents approach the deadlock-related undesirable equilibrium, the framework can detect the equilibrium and drive agents away before that happens. This is achieved by a secondary deadlock resolution design with an auxiliary CBF to prevent the multi-agent systems from converging to the undesirable equilibrium. To avoid dominating effects due to the deadlock resolution over the original task-related controllers, a deadlock indicator function using CBF-inspired risk measurement is proposed and encoded in the unified framework for the agents to adaptively determine when to activate the deadlock resolution. This allows the agents to follow their original control tasks and seamlessly unlock or deactivate deadlock resolution as necessary, effectively improving task efficiency. We demonstrate the effectiveness of the proposed method through theoretical analysis, numerical simulations, and real-world experiments.

ICRA Conference 2025 Conference Paper

Computationally and Sample Efficient Safe Reinforcement Learning Using Adaptive Conformal Prediction

Hao Zhou
Yanze Zhang
Wenhao Luo

Safety is a critical concern in learning-enabled autonomous systems especially when deploying these systems in real-world scenarios. An important challenge is accurately quantifying the uncertainty of unknown models to generate provably safe control policies that facilitate the gathering of informative data, thereby achieving both safe and optimal policies. Additionally, the selection of the data-driven model can significantly impact both the real-time implementation and the uncertainty quantification process. In this paper, we propose a provably sample efficient episodic safe learning framework that remains robust across various model choices with quantified uncertainty for online control tasks. Specifically, we first employ Quadrature Fourier Features (QFF) for kernel function approximation of Gaussian Processes (GPs) to enable efficient approximation of unknown dynamics. Then the Adaptive Conformal Prediction (ACP) is used to quantify the uncertainty from online observations and combined with the Control Barrier Functions (CBF) to characterize the uncertainty-aware safe control constraints under learned dynamics. Finally, an optimism-based exploration strategy is integrated with ACP-based CBFs for safe exploration and near-optimal safe nonlinear control. Theoretical proofs and simulation results are provided to demonstrate the effectiveness and efficiency of the proposed framework.

ICRA Conference 2025 Conference Paper

Distributed Multi-Robot Source Seeking in Unknown Environments with Unknown Number of Sources

Lingpeng Chen
Siva Kailas
Srujan Deolasee
Wenhao Luo
Katia P. Sycara
Woojun Kim

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DOSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function.

ICRA Conference 2025 Conference Paper

Integrating Multi-Robot Adaptive Sampling and Informative Path Planning for Spatiotemporal Natural Environment Prediction

Siva Kailas
Srujan Deolasee
Wenhao Luo
Woojun Kim
Katia P. Sycara

Learning to predict spatiotemporal (ST) environmental processes from a sparse set of samples collected autonomously is a difficult task from both a sampling perspective (collecting the best sparse samples) and from a learning perspective (predicting the next timestep). In this work, we focus on investigating the sample collection process via multirobot informative path planning. We present an approach for incorporating multi-robot informative path planning into a spatiotemporal adaptive sampling framework while considering path length constraints for sampling location selection. We also incorporate informative path planning to determine the best path to collect samples along while en route to collecting the desired sample. We achieve this in a decentralized manner by decoupling the process into two stages: the first stage uses our spatiotemporal mixture of Gaussian Processes (STMGP) model to determine the most informative sampling location via a mutual information lower bound heuristic and the second stage plans an informative path to collect the desired sample and other additional informative samples via submodular function optimization. Moreover, we effectively leverage peer-to-peer communication to enable coordination. Simulation results on real-world spatiotemporal data are provided to validate the effectiveness of our proposed approach.

IROS Conference 2025 Conference Paper

Merry-Go-Round: Safe Control of Decentralized Multi-Robot Systems with Deadlock Prevention

Wonjong Lee
Joonyeol Sim
Joonkyung Kim
Siwon Jo
Wenhao Luo
Changjoo Nam

We propose a hybrid approach for decentralized multi-robot navigation that ensures both safety and deadlock prevention. Building on a standard control formulation, we add a lightweight deadlock prevention mechanism by forming temporary "roundabouts" (circular reference paths). Each robot relies only on local, peer-to-peer communication and a controller for base collision avoidance; a roundabout is generated or joined on demand to avert deadlocks. Robots in the roundabout travel in one direction until an escape condition is met, allowing them to return to goal-oriented motion. Unlike classical decentralized methods that lack explicit deadlock resolution, our roundabout maneuver ensures system-wide forward progress while preserving safety constraints. Extensive simulations and physical robot experiments show that our method consistently outperforms or matches the success and arrival rates of other decentralized control approaches, particularly in cluttered or high-density scenarios, all with minimal centralized coordination.

IROS Conference 2024 Conference Paper

Integrating Online Learning and Connectivity Maintenance for Communication-Aware Multi-Robot Coordination

Yupeng Yang
Yiwei Lyu 0002
Yanze Zhang
Ian Gao
Wenhao Luo

This paper proposes a novel data-driven control strategy for maintaining connectivity in networked multi-robot systems. Existing approaches often rely on a predetermined communication model specifying whether pairwise robots can communicate given their relative distance to guide the connectivity-aware control design, which may not capture real-world communication conditions. To relax that assumption, we present the concept of Data-driven Connectivity Barrier Certificates, which utilize Control Barrier Functions (CBF) and Gaussian Processes (GP) to characterize the admissible control space for pairwise robots based on communication performance observed online. This allows robots to maintain a satisfying level of pairwise communication quality (measured by the received signal strength) while in motion. Then we propose a Data-driven Connectivity Maintenance (DCM) algorithm that combines (1) online learning of the communication signal strength and (2) a bi-level optimization-based control framework for the robot team to enforce global connectivity of the realistic multi-robot communication graph and minimally deviate from their task-related motions. We provide theoretical proofs to justify the properties of our algorithm and demonstrate its effectiveness through simulations with up to 20 robots.

AAMAS Conference 2023 Conference Paper

Decentralized Safe Navigation for Multi-agent Systems via Risk-aware Weighted Buffered Voronoi Cells

Yiwei Lyu
John M. Dolan
Wenhao Luo

In this paper, we propose Risk-aware Weighted Buffered Voronoi tessellation, a variant of Generalized Voronoi tessellation, for decentralized multi-agent collision-free navigation. Inherited from the traditional Voronoi tessellation, a safety guarantee in terms of inter-robot collision avoidance is achieved by partitioning the joint state space of the multi-agent system into individual cells that constrain each individual agent’s motion in a distributed manner. Different from many existing Voronoi tessellations-based collision avoidance approaches, our Risk-aware Weighted Buffered Voronoi Cell (Risk-aware WBVC) partition not only takes agent positional information into account, but also the motion information when determining the cell boundaries between pairwise robots. Our riskaware WBVC relies on the novel use of Control Barrier Functions (CBF) as a measure of risk evaluation that captures to what extent the safety constraints are satisfied between pairwise robots. With that, the cell boundaries of risk-aware WBVC are determined by (1) the varying levels of relative efforts between pairwise agents to respond to potential collisions, and (2) the accumulated risk each agent experiences that is caused by the surrounding agents. This allows for an adaptive constrained space partition among robots that balances between individual’s efforts in respecting the safety constraints and the overall threats due to other agents in the environment, e. g. an aggressive robot moving with higher speed requires a relatively larger space for responding to potential collisions, and a less-threatened robot may be expected to yield and make more room for those exposed to higher risk. Rigorous proofs of formal safety guarantees are provided and simulations are demonstrated on up to 16 robots to show the effectiveness of our method.

ICRA Conference 2023 Conference Paper

Minimally Constrained Multi-Robot Coordination with Line-of-Sight Connectivity Maintenance

Yupeng Yang
Yiwei Lyu 0002
Wenhao Luo

In this paper, we consider a team of mobile robots executing simultaneously multiple behaviors by different subgroups, while maintaining global and subgroup line-of-sight (LOS) network connectivity that minimally constrains the original multi-robot behaviors. The LOS connectivity between pairwise robots is preserved when two robots stay within the limited communication range and their LOS remains occlusion-free from static obstacles while moving. By using control barrier functions (CBF) and minimum volume enclosing ellipsoids (MVEE), we first introduce the LOS connectivity barrier certificate (LOS-CBC) to characterize the state-dependent admissible control space for pairwise robots, from which their resulting motion will keep the two robots LOS connected over time. We then propose the Minimum Line-of-Sight Connectivity Constraint Spanning Tree (MLCCST) as a step-wise bilevel optimization framework to jointly optimize (a) the minimum set of LOS edges to actively maintain, and (b) the control revision with respect to a nominal multi-robot controller due to LOS connectivity maintenance. As proved in the theoretical analysis, this allows the robots to improvise the optimal composition of LOS-CBC control constraints that are least constraining around the nominal controllers, and at the same time enforce the global and subgroup LOS connectivity through the resulting preserved set of pairwise LOS edges. The framework thus leads to robots staying as close to their nominal behaviors, while exhibiting dynamically changing LOS-connected network topology that provides the greatest flexibility for the existing multi-robot tasks in real-time. We demonstrate the effectiveness of our approach through simulations with up to 64 robots.

AAMAS Conference 2023 Conference Paper

Minimally Constraining Line-of-Sight Connectivity Maintenance for Collision-free Multi-Robot Networks under Uncertainty

Yupeng Yang
Yiwei Lyu
Wenhao Luo

In this paper, we consider the Line-of-Sight (LOS) connectivity maintenance under positional uncertainty for a team of robots consisting of multiple subgroups with given parallel tasks. The LOS connectivity between pairwise robots is preserved when the two robots are within the limited communication range and their LOS is occlusion-free from static obstacles over time. By unifying a control theoretic approach and a graph theoretic approach, we develop an Uncertainty Aware Line-of-Sight Minimum Spanning Tree (LOS- MST) framework to compute robots’ motion that maintains only a minimally constraining set of LOS edges among robots for global and subgroup LOS connectivity, while minimizing the motion disruption to their original multi-robot behaviors. Simulation results are provided to validate the effectiveness of our proposed approach.

AAAI Conference 2023 Short Paper

Risk-Aware Decentralized Safe Control via Dynamic Responsibility Allocation (Student Abstract)

Yiwei Lyu
Wenhao Luo
John M. Dolan

In this work, we present a novel risk-aware decentralized Control Barrier Function (CBF)-based controller for multi-agent systems. The proposed decentralized controller is composed based on pairwise agent responsibility shares (a percentage), calculated from the risk evaluation of each individual agent faces in a multi-agent interaction environment. With our proposed CBF-inspired risk evaluation framework, the responsibility portions between pairwise agents are dynamically updated based on the relative risk they face. Our method allows agents with lower risk to enjoy a higher level of freedom in terms of a wider action space, and the agents exposed to higher risk are constrained more tightly on action spaces, and are therefore forced to proceed with caution.

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IROS Conference 2023 Conference Paper

Risk-Aware Safe Control for Decentralized Multi-Agent Systems via Dynamic Responsibility Allocation

Yiwei Lyu 0002
Wenhao Luo
John M. Dolan

Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems. However, in the absence of an explicit global coordinator, it is hard for distributed agents to determine how to efficiently interact with others. In this paper, we present a risk-aware decentralized control framework that provides guidance on how much relative responsibility share (a percentage) an individual agent should take to avoid collisions with others while moving efficiently without direct communications. We propose a novel Control Barrier Function (CBF)-inspired risk measurement to characterize the aggregate risk agents face from potential collisions under motion uncertainty. We use this measurement to allocate responsibility shares among agents dynamically and develop risk-aware decentralized safe controllers. In this way, we are able to leverage the flexibility of robots with lower risk to improve the motion flexibility for those with higher risk, thus achieving improved collective safety. We demonstrate the validity and efficiency of our proposed approach through two examples: ramp merging in autonomous driving and a multi-agent position-swapping game.

AAAI Conference 2023 Conference Paper

Towards Safe and Resilient Autonomy in Multi-Robot Systems

Wenhao Luo

In the near future, autonomous systems such as multi-robot systems are envisioned to increasingly co-exist with hu- mans in our daily lives, from household service to large- scale warehouse logistics, agriculture environment sampling, and smart city. In these applications, robots and humans as networked heterogeneous components will frequently inter- act with each other in a variety of scenarios under uncer- tain, rapidly-changing, and possibly hostile environment. On one hand, harmonious interactions among robots, as well as between robots and humans, would require safe integration (e.g. collision-free close-proximity interactions) of heteroge- neous robots, human, and human-robot autonomy. On the other hand, reliable interactions among autonomous multi- robot systems often call for resilient system integrity (e.g. communication capability with potential robot failures) to re- tain its capability of accomplishing complex tasks through coordinated behaviors. In the proposed talk, I will discuss our recent works towards safe autonomy and resilient autonomy that aim to facilitate correct-by-design robotic behaviors in a variety of applications.

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AAAI Conference 2022 Short Paper

Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract)

Yiwei Lyu
Wenhao Luo
John M. Dolan

Control Barrier Functions have been extensively studied to ensure guaranteed safety during inter-robot interactions. In this paper, we introduce the Parametric-Control Barrier Function (Parametric-CBF), a novel variant of the traditional Control Barrier Function to extend its expressivity in describing different safe behaviors among heterogeneous robots. A parametric-CBF based framework is presented to enable the ego robot to model the neighboring robots behavior and further improve the coordination efficiency during interaction while enjoying formally provable safety guarantees. We demonstrate the usage of Parametric-CBF in behavior prediction and adaptive safe control in the ramp merging scenario.

ICRA Conference 2021 Conference Paper

Distributed Topology Correction for Flexible Connectivity Maintenance in Multi-Robot Systems

Sha Yi
Wenhao Luo
Katia P. Sycara

Multi-robot systems can perform task-related collaborative behaviors while maintaining connectivity within the system. However, some robots may fail to execute tasks or converge relatively slowly due to connectivity constraints. We consider the case that some robots may not have tasks assigned at a certain time frame, and they may help the task robots to achieve their goals by forming a connectivity graph with flexible topology. Therefore, we introduce a topology correction controller to provide flexibility for the task robots to perform task behaviors by modifying the topology of the connectivity graph for a faster convergence rate. We propose a distributed approach of blending weighted rendezvous and weighted flocking to form the correction controller. We prove that this scheme can guarantee a faster convergence rate and provide flexible connectivity graph topology. We then present our result of a system of up to thirty robots in various cluttered environments and show that our approach of behavior combination is robust and scalable.

IROS Conference 2021 Conference Paper

Hiding Leader's Identity in Leader-Follower Navigation through Multi-Agent Reinforcement Learning

Ankur Deka
Wenhao Luo
Huao Li
Michael Lewis 0001
Katia P. Sycara

Leader-follower navigation is a popular class of multi-robot algorithms where a leader robot leads the follower robots in a team. The leader has specialized capabilities or mission critical information (e. g. goal location) that the followers lack, and this makes the leader crucial for the mission’s success. However, this also makes the leader a vulnerability -an external adversary who wishes to sabotage the robot team’s mission can simply harm the leader and the whole robot team’s mission would be compromised. Since robot motion generated by traditional leader-follower navigation algorithms can reveal the identity of the leader, we propose a defense mechanism of hiding the leader’s identity by ensuring the leader moves in a way that behaviorally camouflages it with the followers, making it difficult for an adversary to identify the leader. To achieve this, we combine Multi-Agent Reinforcement Learning, Graph Neural Networks and adversarial training. Our approach enables the multi-robot team to optimize the primary task performance with leader motion similar to follower motion, behaviorally camouflaging it with the followers. Our algorithm outperforms existing work that tries to hide the leader’s identity in a multi-robot team by tuning traditional leader-follower control parameters with Classical Genetic Algorithms. We also evaluated human performance in inferring the leader’s identity and found that humans had lower accuracy when the robot team used our proposed navigation algorithm.

IROS Conference 2021 Conference Paper

Meta Preference Learning for Fast User Adaptation in Human-Supervisory Multi-Robot Deployments

Chao Huang 0027
Wenhao Luo
Rui Liu

As multi-robot systems (MRS) are widely used in various tasks such as natural disaster response and social security, people enthusiastically expect an MRS to be ubiquitous that a general user without heavy training can easily operate. However, humans have various preferences on balancing between task performance and safety, imposing different requirements onto MRS control. Failing to comply with preferences makes people feel difficult in operation and decreases human willingness of using an MRS. Therefore, to improve social acceptance as well as performance, there is an urgent need to adjust MRS behaviors according to human preferences before triggering human corrections, which increases cognitive load. In this paper, a novel Meta Preference Learning (MPL) method was developed to enable an MRS to fast adapt to user preferences. MPL based on meta learning mechanism can quickly assess human preferences from limited instructions; then, a neural network based preference model adjusts MRS behaviors for preference adaption. To validate method effectiveness, a task scenario "An MRS searches victims in an earthquake disaster site" was designed; 20 human users were involved to identify preferences as {"aggressive", "medium", "reserved"}; based on user guidance and domain knowledge, about 20, 000 preferences were simulated to cover different operations related to {"task quality", "task progress", "robot safety"}. The effectiveness of MPL in preference adaption was validated by the reduced duration and frequency of human interventions.

ICRA Conference 2021 Conference Paper

Online Connectivity-aware Dynamic Deployment for Heterogeneous Multi-Robot Systems

Chendi Lin
Wenhao Luo
Katia P. Sycara

In this paper, we consider the dynamic multi-robot distribution problem where a heterogeneous group of networked robots is tasked to spread out and simultaneously move towards multiple moving task areas while maintaining connectivity. The heterogeneity of the system is characterized by various categories of units and each robot carries different numbers of units per category representing heterogeneous capabilities. Every task area with different importance demands a total number of units contributed by all of the robots within its area. Moreover, we assume the importance and the total number of units requested from each task area is initially unknown. The robots need first to explore, i. e. , reach those areas, and then be allocated to the tasks so to fulfill the requirements. The multi-robot distribution problem is formulated as designing controllers to distribute the robots that maximize the overall task fulfillment while minimizing the traveling costs in presence of connectivity constraints. We propose a novel connectivity-aware multi-robot redistribution approach that accounts for dynamic task allocation and connectivity maintenance for a heterogeneous robot team. Such an approach could generate sub-optimal robot controllers so that the amount of total unfulfilled requirements of the tasks weighted by their importance is minimized and robots stay connected at all times. Simulation and numerical results are provided to demonstrate the effectiveness of the proposed approaches.

ICRA Conference 2021 Conference Paper

Probabilistic Safety-Assured Adaptive Merging Control for Autonomous Vehicles

Yiwei Lyu 0002
Wenhao Luo
John M. Dolan

Autonomous vehicles face tremendous challenges while interacting with human drivers in different kinds of scenarios. Developing control methods with safety guarantees while performing interactions with uncertainty is an ongoing research goal. In this paper, we present a real-time safe control framework using bi-level optimization with Control Barrier Function (CBF) that enables an autonomous ego vehicle to interact with human-driven cars in ramp merging scenarios with a consistent safety guarantee. In order to explicitly address motion uncertainty, we propose a novel extension of control barrier functions to a probabilistic setting with provable chance-constrained safety and analyze the feasibility of our control design. The formulated bi-level optimization framework entails first choosing the ego vehicle's optimal driving style in terms of safety and primary objective, and then minimally modifying a nominal controller in the context of quadratic programming subject to the probabilistic safety constraints. This allows for adaptation to different driving strategies with a formally provable feasibility guarantee for the ego vehicle's safe controller. Experimental results are provided to demonstrate the effectiveness of our proposed approach.

IROS Conference 2020 Conference Paper

Adaptive Informative Sampling with Environment Partitioning for Heterogeneous Multi-Robot Systems

Yunfei Shi
Ning Wang
Jianmin Zheng
Yang Zhang
Sha Yi
Wenhao Luo
Katia P. Sycara

Multi-robot systems are widely used in environmental exploration and modeling, especially in hazardous environments. However, different types of robots are limited by different mobility, battery life, sensor type, etc. Heterogeneous robot systems are able to utilize various types of robots and provide solutions where robots are able to compensate each other with their different capabilities. In this paper, we consider the problem of sampling and modeling environmental characteristics with a heterogeneous team of robots. To utilize heterogeneity of the system while remaining computationally tractable, we propose an environmental partitioning approach that leverages various robot capabilities by forming a uniformly defined heterogeneity cost space. We combine with the mixture of Gaussian Processes model-learning framework to adaptively sample and model the environment in an efficient and scalable manner. We demonstrate our algorithm in field experiments with ground and aerial vehicles.

ICRA Conference 2020 Conference Paper

Behavior Mixing with Minimum Global and Subgroup Connectivity Maintenance for Large-Scale Multi-Robot Systems

Wenhao Luo
Sha Yi
Katia P. Sycara

In many cases the multi-robot systems are desired to execute simultaneously multiple behaviors with different controllers, and sequences of behaviors in real time, which we call behavior mixing. Behavior mixing is accomplished when different subgroups of the overall robot team change their controllers to collectively achieve given tasks while maintaining connectivity within and across subgroups in one connected communication graph. In this paper, we present a provably minimum connectivity maintenance framework to ensure the subgroups and overall robot team stay connected at all times while providing the highest freedom for behavior mixing. In particular, we propose a real-time distributed Minimum Connectivity Constraint Spanning Tree (MCCST) algorithm to select the minimum inter-robot connectivity constraints preserving subgroup and global connectivity that are least likely to be violated by the original controllers. With the employed safety and connectivity barrier certificates for the activated connectivity constraints and collision avoidance, the behavior mixing controllers are thus minimally modified from the original controllers. We demonstrate the effectiveness and scalability of our approach via simulations of up to 100 robots with multiple behaviors.

IROS Conference 2020 Conference Paper

Minimally Disruptive Connectivity Enhancement for Resilient Multi-Robot Teams

Wenhao Luo
Nilanjan Chakraborty
Katia P. Sycara

In this work, we focus on developing algorithms to maintain and enhance the connectivity of a multi-robot system with minimal disruption to the primary tasks that the robots are performing. Such algorithms are useful for collaborating robots to be resilient to reduction in connectivity of the communication graph of the robot team when robots can arrive or leave. These algorithms are also useful in a supervisory control setting when an operator wants to enhance the connectivity of the robot team. In contrast to many existing works that can only maintain the current connectivity of the multi-robot graph, we propose a generalized connectivity control framework that allows for reconfiguration of the multi-robot system to provably satisfy any connectivity demand, while minimally disrupting the execution of their original tasks. In particular, we propose a novel k-Connected Minimum Resilient Graph (k-CMRG) algorithm to compute an optimal k-connectivity graph that minimally constrains the robots' original task-related motion, and employ the Finite-Time Convergence Control Barrier Function (FCBF) to enforce the pairwise robot motion constraints defined by the edges of the graph. The original controllers are minimally modified to drive the robots and form the k-CMRG. We demonstrate the effectiveness of our approach via simulations in the presence of multiple tasks and robot failures.

NeurIPS Conference 2020 Conference Paper

Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

Wenhao Luo
Wen Sun
Ashish Kapoor

Safety in terms of collision avoidance for multi-robot systems is a difficult challenge under uncertainty, non-determinism, and lack of complete information. This paper aims to propose a collision avoidance method that accounts for both measurement uncertainty and motion uncertainty. In particular, we propose Probabilistic Safety Barrier Certificates (PrSBC) using Control Barrier Functions to define the space of admissible control actions that are probabilistically safe with formally provable theoretical guarantee. By formulating the chance constrained safety set into deterministic control constraints with PrSBC, the method entails minimally modifying an existing controller to determine an alternative safe controller via quadratic programming constrained to PrSBC constraints. The key advantage of the approach is that no assumptions about the form of uncertainty are required other than finite support, also enabling worst-case guarantees. We demonstrate effectiveness of the approach through experiments on realistic simulation environments.

AAMAS Conference 2019 Conference Paper

Active Learning with Gaussian Processes for High Throughput Phenotyping

Sumit Kumar
Wenhao Luo
George Kantor
Katia Sycara

A looming question that must be solved before robotic plant phenotyping capabilities can have significant impact to crop improvement programs is scalability. High Throughput Phenotyping (HTP) uses robotic technologies to analyze crops in order to determine species with favorable traits, however, the current practices rely on exhaustive coverage and data collection from the entire crop field being monitored under the breeding experiment. This works well in relatively small agricultural fields but can not be scaled to the larger ones, thus limiting the progress of genetics research. In this work, we propose an active learning algorithm to enable an autonomous system to collect the most informative samples in order to accurately learn the distribution of phenotypes in the field with the help of a Gaussian Process model. We demonstrate the superior performance of our proposed algorithm compared to the current practices on sorghum phenotype data collection.

AAMAS Conference 2019 Conference Paper

Distributed Environmental Modeling and Adaptive Sampling for Multi-Robot Sensor Coverage

Wenhao Luo
Changjoo Nam
George Kantor
Katia Sycara

We consider the problem of online distributed environmental modeling and adaptive sampling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the sensing performance over environmental phenomena, whose distribution is often referred to as a density function. Unlike most existing works that either assume certain knowledge of the density function beforehand or centrally learn the density function assuming global knowledge of collected data from all the robots, we propose a fully distributed adaptive sampling approach to allow robots to efficiently learn the unknown density function online. In particular, we developed adaptive coverage controllers based on the learned density functions for minimizing the sensing cost. To capture significantly different components of the environmental phenomenon with only locally collected data for each robot when global knowledge is not available, we propose a distributed mixture of Gaussian Processes algorithm that enables robots to collaboratively learn the global density function by exchanging only modelrelated parameters. We empirically demonstrate the effectiveness of our algorithm via evaluation on real-world data gathered from agricultural field robot and indoor static sensors.

IROS Conference 2019 Conference Paper

Heuristic-based Multiple Mobile Depots Route Planning for Recharging Persistent Surveillance Robots

Yifan Ding
Wenhao Luo
Katia P. Sycara

Persistent surveillance of a target space using multiple unmanned aerial vehicles (UAVs) has multiple applications such as geographical surveys, air quality monitoring, and security monitoring. The limited onboard battery capacity challenges the continuous operation in these applications of persistent robots. We consider the problem for recharging persistent robots using mobile depots. The mobile depots collectively compute a set of tours to recharge all persistent robots with the minimum total cost. Compared to other works, the persistent UAVs are not required to detour to a static depot for energy replenishment such as recharging or battery swapping. We formulate this problem as a Generalized Multiple Depots Travelling Salesman Problem (GMDTSP) on a complete graph. A heuristic-based algorithm Multiple Depots Random Select (RSMD) is proposed to solve the recharging problem efficiently. The RSMD has proved to have an analytical constant upper bound in the worst-case scenario. We also propose a post-processing heuristic (RSMD-IM) to improve the solution quality further. We demonstrate the efficiency and effectiveness of our algorithm via benchmark on multiple instances from TSPLIB and GTSPLIB. The simulation results show that RSMD and RSMD-IM will perform significantly faster than the state of the art heuristic solver LKH with a loss of about 10% of solution quality.

IROS Conference 2019 Conference Paper

Minimum k-Connectivity Maintenance for Robust Multi-Robot Systems

Wenhao Luo
Katia P. Sycara

In many multi-robot applications, it is critical to maintain connectivity within the robotic team to allow for information exchange and coordination. While most of the existing works focus on connectivity control that ensures robotic team remain connected as one component without faults, we consider the problem of robust connectivity maintenance that seeks to maintain k-connectivity, such that the multi-robot network could stay connected with the removal of fewer than k robots. In this paper, we propose provably minimum k-connectivity maintenance algorithms for multi-robot systems. This ensures the robustness of the multi-robot network connectivity at all time and also in a flexible and optimal way to provide the highest freedom for robots task-related controllers. Particularly, we propose a k-Connected Minimum Constraints Subgraph (k-CMCS) algorithm that activates the minimum k-connectivity constraints to the original controllers, and then revise the original controllers in a minimally invasive fashion. We demonstrate the effectiveness of our approach via simulations of up to 40 robots in the presence of multiple behaviors.

AAMAS Conference 2019 Conference Paper

Trust-Aware Behavior Reflection for Robot Swarm Self-Healing

Rui Liu
Fan Jia
Wenhao Luo
Meghan Chandarana
Changjoo Nam
Michael Lewis
Katia Sycara

The deployment of robot swarms is influenced by real-world factors, such as motor issues, sensor failure, and wind disturbances. These factors cause the appearance of faulty robots. In a decentralized swarm, sharing incorrect information from faulty robots will lead to undesired swarm behaviors, such as swarm disconnection and incorrect heading directions. We envision a system where a human operator is exerting supervisory control over a remote swarm by indicating changes in trust to the swarm via a "trust-signal". By correcting faulty behaviors, trust between the human and the swarm is maintained to facilitate human-swarm cooperation. In this research, a trust-aware behavior reflection method – Trust-R – is designed based on a weighted mean subsequence reduced algorithm (WMSR). By using Trust-R, detected faulty behaviors are automatically corrected by the swarm in a decentralized fashion by referring to the motion status of their trusted neighbors and isolating failed robots from the others. Based on real-world scenarios, three types of robot faults – degraded performance caused by motor wear, abnormal motion caused by system uncertainty and motion deviation caused by an external disturbance such as wind – were simulated to test the effectiveness of Trust-R. Results show that Trust-R is effective in correcting swarm behaviors for swarm self-healing.

ICRA Conference 2018 Conference Paper

Adaptive Sampling and Online Learning in Multi-Robot Sensor Coverage with Mixture of Gaussian Processes

Wenhao Luo
Katia P. Sycara

We consider the problem of online environmental sampling and modeling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the overall sensing performance. In contrast to most existing works on multi-robot coverage control that assume prior knowledge of the distribution of environmental phenomenon, also known as density function, we relax this assumption and enable the robot team to efficiently learn the model of the unknown density function Online using adaptive sampling and non-parametric inference such as Gaussian Process (GP). To capture significantly different components of the environmental phenomenon, we propose a new approach with mixture of locally learned Gaussian Processes for collective model learning and an information-theoretic criterion for simultaneous adaptive sampling in multi-robot coverage. Our approach demonstrates a better generalization of the environment modeling and thus the improved performance of coverage without assuming the density function is known a priori. We demonstrate the effectiveness of our algorithm via simulations of information gathering from indoor static sensors.

ICRA Conference 2017 Conference Paper

Decentralized coordinated motion for a large team of robots preserving connectivity and avoiding collisions

Anqi Li 0001
Wenhao Luo
Sasanka Nagavalli
Katia P. Sycara

We consider the general problem of moving a large number of networked robots toward a goal position through a cluttered environment while preserving network communication connectivity and avoiding both inter-robot collisions and collision with obstacles. In contrast to previous approaches that either plan complete paths for each individual robot in the high-dimensional joint configuration space or control the robot group as a whole with explicit constraints on the group's boundary and inter-robot pairwise distance, we propose a novel decentralized online behavior-based algorithm that relies on the topological structure of the multi-robot communication and sensing graphs to solve this problem. We formally describe the communication graph as a simplicial complex that enables robots to iteratively identify the frontier nodes and coordinate forward motion through the sensing graph. This approach is proved to automatically deform robot teams for collision avoidance and always preserve connectivity. The effectiveness of our approach is demonstrated using numerical simulations. The algorithm is shown to scale linearly in the number of robots.

IROS Conference 2016 Conference Paper

Distributed knowledge leader selection for multi-robot environmental sampling under bandwidth constraints

Wenhao Luo
Shehzaman S. Khatib
Sasanka Nagavalli
Nilanjan Chakraborty
Katia P. Sycara

In many multi-robot applications such as target search, environmental monitoring and reconnaissance, the multi-robot system operates semi-autonomously, but under the supervision of a remote human who monitors task progress. In these applications, each robot collects a large amount of task-specific data that must be sent to the human periodically to keep the human aware of task progress. It is often the case that the human-robot communication links are extremely bandwidth constrained and/or have significantly higher latency than inter-robot communication links, so it is impossible for all robots to send their task-specific data together. Thus, only a subset of robots, which we call the knowledge leaders, can send their data at a time. In this paper, we study the knowledge leader selection problem, where the goal is to select a subset of robots with a given cardinality that transmits the most informative task-specific data for the human. We prove that the knowledge leader selection is a submodular function maximization problem under explicit conditions and present a novel distributed submodular optimization algorithm that has the same approximation guarantees as the centralized greedy algorithm. The effectiveness of our approach is demonstrated using numerical simulations.