Author name cluster

Matthew J. Travers

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

23 papers

1 author row

IROS Conference 2025 Conference Paper

CAP: A Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments using Coverage Guidance Graph

Zongyuan Shen
Burhanuddin Shirose
Prasanna Sriganesh
Matthew J. Travers

Efficient coverage of unknown environments requires robots to adapt their paths in real time based on on-board sensor data. In this paper, we introduce CAP, a connectivity-aware hierarchical coverage path planning algorithm for efficient coverage of unknown environments. During online operation, CAP incrementally constructs a coverage guidance graph to capture essential information about the environment. Based on the updated graph, the hierarchical planner determines an efficient path to maximize global coverage efficiency and minimize local coverage time. The performance of CAP is evaluated and compared with five baseline algorithms through high-fidelity simulations as well as robot experiments. Our results show that CAP yields significant improvements in coverage time, path length, and path overlap ratio.

IROS Conference 2023 Conference Paper

EELS: Towards Autonomous Mobility in Extreme Terrain with a Versatile Snake Robot with Resilience to Exteroception Failures

Rohan Thakker
Michael Paton
Marlin P. Strub
R. Michael Swan
Guglielmo Daddi
Rob Royce
L. Phillipe Tosi
Matthew Gildner

The discovery of ocean worlds such as Enceladus, Titan, and Europa motivates the development of versatile autonomous mobility systems to enable the next era of space exploration where there is large uncertainty in terrain specifications due to a lack of prior surface reconnaissance missions. To explore these environments, we propose Exobiology Extant Life Surveyor (EELS): the first large-scale (4 lm long with 400 Nm peak torque) snake robot. The large scale is achieved by using a screw-based active skin mechanism to decouple motion and shape control. Autonomous mobility for such a system remains an open problem due to its many Degrees of Freedom (DoFs), complex terrain interactions, and intermittent localization failures in GPS-denied perceptually degraded environments due to the presence of fog, dust, featureless terrains, etc. We propose NEO, an autonomy architecture that scales to large DoFs to generate a versatile set of gaits to achieve mobility in unknown extreme environments. We also discuss the resilience capabilities of NEO that achieves closed-loop tracking performance by leveraging exteroception when available but can also operate with proprioception only, leading to resiliency against localization failures via graceful degradation in performance rather than unsafe behaviors. A quantitative hardware evaluation of exteroceptive leader-follower gait is performed indoors on synthetic ice along with qualitative results of field deployment of the proprioceptive leader-follower and sidewinding gaits in extreme environments of icy and sandy terrains with mobility-stressing elements such as trenches, undulations, and steep slopes (up to 35 degrees). We present a set of lessons learned from field deployments with a summary of challenges and open research problems. Video: www. rohanthakker. in/eels-neo-autonomy. html

ICRA Conference 2022 Conference Paper

Modular Robot Design Optimization with Generative Adversarial Networks

Jiaheng Hu
Julian Whitman
Matthew J. Travers
Howie Choset

Modular robots are made up of a set of components which can be configured and reconfigured to form customized robots for a wide range of tasks. Fully utilizing the flexibility of modular robots is challenging, as it requires the identification of optimal modular designs for each given task, often with limited computation and time. Previous works in design automation achieve efficient run-times by utilizing machine learning to create a one-to-one mapping from task to design. However, the problem of robot design is often multimodal, where multiple distinct designs can be similarly or equally good for a task. Alternative design solutions may be needed in the field, for instance, if a module in the optimal design fails and no replacement is available. This paper presents a novel method based on generative adversarial networks (GANs) that learns a one-to-many mapping from task to a distribution of designs. We apply our method to construct locomoting modular robots for terrains with varying obstacle heights and infill. We compare our method against the state-of-the-art, and find that our algorithm results in better solution quality, diversity, and alternatives for when the optimal design fails.

ICRA Conference 2022 Conference Paper

Periodic SLAM: Using Cyclic Constraints to Improve the Performance of Visual-Inertial SLAM on Legged Robots

Hans Kumar
J. Joe Payne
Matthew J. Travers
Aaron M. Johnson 0001
Howie Choset

Methods for state estimation that rely on visual information are challenging on legged robots due to rapid changes in the viewing angle of onboard cameras. In this work, we show that by leveraging structure in the way that the robot locomotes, the accuracy of visual-inertial SLAM in these challenging scenarios can be increased. We present a method that takes advantage of the underlying periodic predictability often present in the motion of legged robots to improve the performance of the feature tracking module within a visual-inertial SLAM system. Our method performs multi-session SLAM on a single robot, where each session is responsible for mapping during a distinct portion of the robot's gait cycle. Our method produces lower absolute trajectory error than several state-of-the-art methods for visual-inertial SLAM in both a simulated environment and on data collected on a quadrupedal robot executing dynamic gaits. On real-world bounding gaits, our median trajectory error was less than 35% of the error of the next best estimate provided by state-of-the-art methods.

IROS Conference 2022 Conference Paper

RGB-X Classification for Electronics Sorting

Abhimanyu
Tejas Zodage
Umesh Thillaivasan
Xinyue Lai
Rahul Chakwate
Javier Santillan
Emma Oti
Ming Zhao

Effectively disassembling and recovering materials from waste electrical and electronic equipment (WEEE) is a critical step in moving global supply chains from carbon-intensive, mined materials to recycled and renewable ones. Conventional recycling processes rely on shredding and sorting waste streams, but for WEEE, which is comprised of numerous dissimilar materials, we explore targeted disassembly of numerous objects for improved material recovery. Many WEEE objects share many key features and therefore can look quite similar, but their material composition and internal component layout can vary, and thus it is critical to have an accurate classifier for subsequent disassembly steps for accurate material separation and recovery. This work introduces RGB-X, a multi-modal image classification approach, that utilizes key features from external RGB images with those generated from X-ray images to accurately classify electronic objects. More specifically, this work develops Iterative Class Activation Mapping (iCAM), a novel network architecture that explicitly focuses on the finer-details in the multi-modal feature maps that are needed for accurate electronic object classification. In order to train a classifier, electronic objects lack large and well annotated X-ray datasets due to expense and need of expert guidance. To overcome this issue, we present a novel way of creating a synthetic dataset using domain randomization applied to the X-ray domain. The combined RGB-X approach gives us an accuracy of 98. 6% on 10 generations of modern smartphones, which is greater than their individual accuracies of 89. 1% (RGB) and 97. 9% (X-ray) independently. We provide experimental results 1 3 3 Experimental work done at Biorobotics Lab, Robotics Institute, Carnegie Mellon University to corroborate our results.

ICRA Conference 2020 Conference Paper

Hierarchical Coverage Path Planning in Complex 3D Environments

Chao Cao
Ji Zhang 0003
Matthew J. Travers
Howie Choset

State-of-the-art coverage planning methods perform well in simple environments but take an ineffectively long time to converge to an optimal solution in complex three-dimensional (3D) environments. As more structures are present in the same volume of workspace, these methods slow down as they spend more time searching for all of the nooks and crannies concealed in three-dimensional spaces. This work presents a method for coverage planning that employs a multi-resolution hierarchical framework to solve the problem at two different levels, producing much higher efficiency than the state-of-the-art. First, a high-level algorithm separates the environment into multiple subspaces at different resolutions and computes an order of the subspaces for traversal. Second, a low-level sampling-based algorithm solves for paths within the subspaces for detailed coverage. In experiments, we evaluate our method using real-world datasets from complex three-dimensional scenes. Our method finds paths that are constantly shorter and converges at least ten times faster than the state-of-the-art. Further, we show results of a physical experiment where a lightweight UAV follows the paths to realize the coverage.

ICRA Conference 2020 Conference Paper

Inferring Task-Space Central Pattern Generator Parameters for Closed-loop Control of Underactuated Robots

Nathan D. Kent
Raunaq M. Bhirangi
Matthew J. Travers
Thomas M. Howard

The complexity associated with the control of highly-articulated legged robots scales quickly as the number of joints increases. Traditional approaches to the control of these robots are often impractical for many real-time applications. This work thus presents a novel sampling-based planning approach for highly-articulated robots that utilizes a probabilistic graphical model (PGM) to infer in real-time how to optimally modify goal-driven, locomotive behaviors for use in closed-loop control. Locomotive behaviors are quantified in terms of the parameters associated with a network of neural oscillators, or rather a central pattern generator (CPG). For the first time, we show that the PGM can be used to optimally modulate different behaviors in real-time (i. e. , to select of optimal choice of parameter values across the CPG model) in response to changes both in the local environment and in the desired control signal. The PGM is trained offline using a library of optimal behaviors that are generated using a gradient-free optimization framework.

IROS Conference 2020 Conference Paper

Steering Magnetic Robots in Two Axes with One Pair of Maxwell Coils *

Emma Benjaminson
Matthew J. Travers
Rebecca E. Taylor

This work demonstrates a novel approach to steering a magnetic swimming robot in two dimensions with a single pair of Maxwell coils. By leveraging the curvature of the magnetic field gradient, we achieve motion along two axes. This method allows us to control medical magnetic robots using only existing MRI technology, without requiring additional hardware or posing any additional risk to the patient. We implement a switching time optimization algorithm which generates a schedule of control inputs that direct the swimming robot to a goal location in the workspace. By alternating the direction of the magnetic field gradient produced by the single pair of coils per this schedule, we are able to move the swimmer to desired points in two dimensions. Finally, we demonstrate the feasibility of our approach with an experimental implementation on the millimeter scale and discuss future opportunities to expand this work to the microscale, as well as other control problems and real-world applications.

IROS Conference 2020 Conference Paper

The Omega Turn: A Biologically-Inspired Turning Strategy for Elongated Limbless Robots

Tianyu Wang 0010
Baxi Chong
Kelimar Diaz
Julian Whitman
Hang Lu
Matthew J. Travers
Daniel I. Goldman
Howie Choset

Snake robots have the potential to locomote through tightly packed spaces, but turning effectively within unmodelled and unsensed environments remains challenging. Inspired by a behavior observed in the tiny nematode worm C. elegans, we propose a novel in-place turning gait for elongated limbless robots. To simplify the control of the robots' many internal degrees-of-freedom, we introduce a biologically-inspired template in which two co-planar traveling waves are superposed to produce an in-plane turning motion, the omega turn. The omega turn gait arises from modulating the wavelengths and amplitudes of the two traveling waves. We experimentally test the omega turn on a snake robot, and show that this turning gait outperforms previous turning gaits: it results in a larger angular displacement and a smaller area swept by the body over a gait cycle, allowing the robot to turn in highly confined spaces.

ICRA Conference 2018 Conference Paper

Central Pattern Generator With Inertial Feedback for Stable Locomotion and Climbing in Unstructured Terrain

Guillaume Sartoretti
Samuel Shaw
Katie Lam
Naixin Fan
Matthew J. Travers
Howie Choset

Inspired by the locomotor nervous system of vertebrates, central pattern generator (CPG) models can be used to design gaits for articulated robots, such as crawling, swimming or legged robots. Incorporating sensory feedback for gait adaptation in these models can improve the locomotive performance of such robots in challenging terrain. However, many CPG models to date have been developed exclusively for open-loop gait generation for traversing level terrain. In this paper, we present a novel approach for incorporating inertial feedback into the CPG framework for the control of body posture during legged locomotion on steep, unstructured terrain. That is, we adapt the limit cycle of each leg of the robot with time to simultaneously produce locomotion and body posture control. We experimentally validate our approach on a hexapod robot, locomoting in a variety of steep, challenging terrains (grass, rocky slide, stairs). We show how our approach can be used to level the robot's body, allowing it to locomote at a relatively constant speed, even as terrain steepness and complexity prevents the use of an open-loop control strategy.

ICRA Conference 2018 Conference Paper

Distributed Learning for the Decentralized Control of Articulated Mobile Robots

Guillaume Sartoretti
Yunfei Shi
William Paivine
Matthew J. Travers
Howie Choset

Decentralized control architectures, such as those conventionally defined by central pattern generators, independently coordinate spatially distributed portions of articulated bodies to achieve system-level objectives. State of the art distributed algorithms for reinforcement learning employ a different but conceptually related idea; independent agents simultaneously coordinating their own behaviors in parallel environments while asynchronously updating the policy of a system-or, rather, meta-level agent. This work, to the best of the authors' knowledge, is the first to explicitly explore the potential relationship between the underlying concepts in homogeneous decentralized control for articulated locomotion and distributed learning. We present an approach that leverages the structure of the asynchronous advantage actor-critic (A3C) algorithm to provide a natural framework for learning decentralized control policies on a single platform. Our primary contribution shows an individual agent in the A3C algorithm can be defined by an independently controlled portion of the robot's body, thus enabling distributed learning on a single platform for efficient hardware implementation. To this end, we show how the system is trained offline using hardware experiments implementing an autonomous decentralized compliant control framework. Our experimental results show that the trained agent outperforms the compliant control baseline by more than 40% in terms of steady progression through a series of randomized, highly cluttered evaluation environments.

ICRA Conference 2018 Conference Paper

Proprioceptive-Inertial Autonomous Locomotion for Articulated Robots

Francesco Ruscelli
Guillaume Sartoretti
Junyu Nan
Zhixin Feng
Matthew J. Travers
Howie Choset

Inspired by the ability of animals to rely on proprioception and vestibular feedback to adapt their gait, we propose a modular framework for autonomous locomotion that relies on force sensing and inertial information. A first controller exploits anti-compliance, a new application of positive force feedback, to quickly react against obstacles upon impact. We hypothesize that, in situations where a robot experiences occasional impacts with the environment, anti-compliance can help negotiate unknown obstacles, similar to biological systems where positive feedback enables fast responses to external stimuli. A novel parallel controller, based on a bi-stable dynamical system, continuously adjusts the robot's direction of locomotion, and reverts it in reaction to major swerves. We present experimental results, demonstrating how our framework allows a snake robot to autonomously locomote through a row of unevenly-spaced obstacles. Finally, we extend our proprioceptive controller to legged locomotion, showing how a hexaprint robot can adapt its motion to climb over obstacles.

ICRA Conference 2015 Conference Paper

Limbless locomotors that turn in place

Chaohui Gong
Matthew J. Travers
Henry C. Astley
Daniel I. Goldman
Howie Choset

Our research group has started a collaboration that analyzes data collected from biological snakes to provide insight on how to better program snake robots. Most data collected on biological snakes views the snakes from above and thus can only detect motion in the horizontal plane. However, both our robots and biological snakes are capable of generating motions both in the horizontal and vertical planes. Vertical waves naturally play a major role in limbless locomotion in that they simultaneously provide thrust motion and make-and-break contact between the mechanism and environment. Analysis on the data, collected from sidewinder rattle snakes, revealed that disparate modes of locomotion emerged from different contact patterns. We conclude that the same horizontal undulation can cause dramatically different motions for both the biological and robotic snakes depending upon the choice of contacts. With this knowledge, we introduce contact scheduling, a technique that plans positions of contacts along the body to design gaits for snake robots. Contact scheduling results in a novel turning gait, which can reorient a snake robot more than 90 degrees in one gait cycle.

ICRA Conference 2015 Conference Paper

Locomotive reduction for snake robots

Xuesu Xiao
Ellen A. Cappo
Weikun Zhen
Jin Dai
Ke Sun 0002
Chaohui Gong
Matthew J. Travers
Howie Choset

Limbless locomotion, evidenced by both biological and robotic snakes, capitalizes on these systems' redundant degrees of freedom to negotiate complicated environments. While the versatility of locomotion methods provided by a snake-like form is of great advantage, the difficulties in both representing the high dimensional workspace configuration and implementing the desired translations and orientations makes difficult further development of autonomous behaviors for snake robots. Based on a previously defined average body frame and set of motion primitives, this work proposes locomotive reduction, a simplifying methodology which reduces the complexity of controlling a redundant snake robot to that of navigating a differential-drive vehicle. We verify this technique by controlling a 16-DOF snake robot using locomotive reduction combined with a visual tracking system. The simplicity resulting from the proposed locomotive reduction method allows users to apply established autonomous navigation techniques previously developed for differential-drive cars to snake robots. Best of all, locomotive reduction preserves the advantages of a snake robot's ability to perform a variety of locomotion modes when facing complicated mobility challenges.

ICRA Conference 2015 Conference Paper

Robot-inspired biology: The compound-wave control template

Jin Dai
Matthew J. Travers
Tony Dear
Chaohui Gong
Henry C. Astley
Daniel I. Goldman
Howie Choset

Biologically inspired robots perform many interesting and useful behaviors, but to effectively emulate their biological counterparts, robots often need to possess many degrees of freedom, complicating their mechanical design and making it difficult to apply standard control and motion planning strategies. To address this complexity, the robotics community has derived low-dimensional parameter-based controllers that naturally coordinate many degrees of freedom such as the serpenoid curves used to control snake robots. Controllers utilizing this parameterization for snake robots have been able to induce behaviors similar to that of the robots' biological counterparts. A similar concept, called a control template, is used in the study of animal movements. However, much of the prior work on control templates has been limited to in-plane motion. In this work, we extend the usage of control templates to three dimensions to both better model and understand biology, as well as to help us gain better intuition into how we can use pre-existing control paradigms to create new behaviors for biologically inspired robots.

ICRA Conference 2015 Conference Paper

Snakeboard motion planning with viscous friction and skidding

Tony Dear
Scott David Kelly
Matthew J. Travers
Howie Choset

The snakeboard is a well-studied example for mechanical systems analysis, largely because of its simultaneous richness in behavior and simplicity in design. However, few snakeboard models incorporate dissipative friction in the traveling direction and skidding as a violation of the rigid nonholonomic constraints. In this paper we investigate these effects on trajectory planning by evaluating a previously proposed friction model as well as a novel skidding model based on the addition of Rayleigh dissipation functions. We show how these additions change the usual behavior of gaits in the forward planning problem, and incorporate the changes into the solutions of the inverse planning problem by utilizing body coordinates along with a curvature parameterization for trajectories.

ICRA Conference 2015 Conference Paper

Use of the nonlinear observability rank condition for improved parametric estimation

Matthew J. Travers
Howie Choset

The correct way to design controllers for dynamic robots is still very much an open question. This is in a large part due to the complexity and uncertainty in modeling their nonlinear dynamics. In this work, we focus on deriving concise dynamic expressions for a particular class of robots that can be used to better reduce uncertainty with respect to unknown parameters in realtime. We accomplish this by using an extended Kalman filtering framework in conjunction with an online controller that continuously maximizes a local measure of nonlinear observability. The main novel contribution of this work is that we directly use the nonlinear observability rank condition to derive the measure of observability at each time step. We are able to make this extension in part by focusing on serial-chain systems and exploiting the geometric structure in their dynamic models. In particular, we derive concise, closed-form and exact analytical representations for the forward dynamics, linearization, and nonlinear observability rank condition of a fixed-base serial manipulator with actively controlled elastic joints. An example is presented in which the spring constants and damping coefficients for a series-elastic actuated manipulator are estimated using the online observability maximizing techniques we derive.

IROS Conference 2014 Conference Paper

Guided locomotion in 3D for snake robots based on contact force optimization

Hugo Ponte
Matthew J. Travers
Howie Choset

The versatility of snake robots has led to their use in a wide variety of settings, including archaeological sites, natural disaster zones, and nuclear power plants. Currently, snake robots locomote through these rugged environments using repeatable pre-programmed motions, often with underwhelming performance. This paper presents the novel design of a control architecture that addresses the limitations of pre-programmed motions by using contact information from the environment. The controller uses contact force optimization, a concept taken from the field of manipulation, in combination with gain scheduling, to perturb existing gait controllers to perform better in three-dimensional environments. The efficacy of the controller is tested in a simulation of a snake robot on rugged terrain.

IROS Conference 2014 Conference Paper

Parameterized controller generation for multiple mode behavior

Chaohui Gong
Matthew J. Travers
Hsien-Tang Kao
Howie Choset

We derive and demonstrate a new capability for snake robots in which two behaviors-one for locomotion and the other for manipulation-are executed simultaneously on the same robot. This is done in two steps: 1) inverse kinematics via numerical optimization and 2) gait-based locomotion via modal decomposition. The result is an analytical representation of a multiple mode behavior that reduces online execution to simple parameterized control. This representation makes it possible to derive a feedback control law that enables reliable visual servoing using a snake robot while climbing a pole.

IROS Conference 2014 Conference Paper

Recursive dynamics and feedback linearizing control of serial-chain manipulators

Matthew J. Travers
Howie Choset

Manipulators that have the compliance necessary to share the same workspace and safely interact with people are currently of great interest to both the industrial as well as research communities. This work focuses on the development of nonlinear controllers for compliant serial-chain manipulators. In particular, we derive a novel algorithm that analytically computes feedback linearizing controllers for N-link manipulators with compliant joints. It is possible to express the controllers in closed form because we use geometric notation to concisely derive an algorithm that analytically inverts the manipulator's generalized inertia matrix. The resultant recursive dynamic expressions make it possible to solve for the feedback linearizing controllers exactly. Simulation results which apply the closed-form controllers to the analytic dynamics of a six-joint serial manipulator with series-elastic actuators are provided.

IROS Conference 2014 Conference Paper

Using Lie algebra for shape estimation of medical snake robots

Rangaprasad Arun Srivatsan
Matthew J. Travers
Howie Choset

Highly articulated robots have the potential to play a key role in minimally invasive surgeries by providing improved access to hard-to-reach anatomy. Estimating their shape inside the body and combining it with 3D preoperative scans of the anatomy enable the surgeon to visualize how the entire robot interacts with the internal organs. As the robot progresses inside the body, the position and orientation of every link comprising the robot, evolves over a coordinate-free Lie algebra, se(3). To capture the full motion and uncertainty of the system, we use an extended Kalman filter where the state vector is defined using elements of se(3). We show that this approach describes the shape of the robot more accurately, than the ones where the state vector is a conventional parametrization, such as Cartesian coordinates and Euler angles. We perform two experiments to demonstrate the effectiveness of this new filtering approach.

ICRA Conference 2014 Conference Paper

Visual sensing for developing autonomous behavior in snake robots

Hugo Ponte
Max Queenan
Chaohui Gong
Christoph Mertz
Matthew J. Travers
Florian Enner
Martial Hebert
Howie Choset

Snake robots are uniquely qualified to investigate a large variety of settings including archaeological sites, natural disaster zones, and nuclear power plants. For these applications, modular snake robots have been tele-operated to perform specific tasks using images returned to it from an onboard camera in the robots head. In order to give the operator an even richer view of the environment and to enable the robot to perform autonomous tasks we developed a structured light sensor that can make three-dimensional maps of the environment. This paper presents a sensor that is uniquely qualified to meet the severe constraints in size, power and computational footprint of snake robots. Using range data, in the form of 3D pointclouds, we show that it is possible to pair high-level planning with mid-level control to accomplish complex tasks without operator intervention.

ICRA Conference 2013 Conference Paper

Extended gait equation for sidewinding

Chaohui Gong
Matthew J. Travers
Xiaozhou Fu
Howie Choset

Sidewinding is an efficient translational gait used by biological snakes to locomote over flat ground. Prior work has identified the fact that it is possible to steer the moving direction of sidewinding. The previously proposed virtual tread model reveals the working principal of sidewinding from a geometric point of view. Unfortunately, the implementation of the virtual tread model relied on a computationally expensive numerical fitting algorithm that impeded online applications. Motivated by this limitation, in this work we propose a novel approach to develop analytical expressions for snake robot gaits based on the study of the corresponding geometric model. This approach is rooted in the identification of dominant frequency components afforded by the two-dimensional Fast Fourier Transformation (FFT). Applying this method to the virtual tread model for conical sidewinding, we derive an analytical expression between the parameters that describe the gait's motion and the turning radius of the system moving in the world. This analytical expression, which we call the extended gait equation, is verified by experimental results.