Home Page for James Motes | Parasol Laboratory


Picture James Motes
PhD Student

Parasol Laboratory url: http://parasollab.web.illinois.edu/~jmotes2/
Department of Computer Science email:
University of Illinois at Urbana-Champaign office: 3307 Siebel Center
Urbana, IL 61801, USA


CV
Google Scholar Profile

I am a PhD student working with Dr. Nancy Amato on multi-robot systems. My research focuses on multi-robot task and motion planning with a focus on leveraging robotic interactions. I joined the lab in 2017 as an undergrad at Texas A&M University and completed both my undergraduate and master's degree in the lab at Texas A&M before moving to Illinois. I started the PhD program at the University of Illinois Urbana-Champaign in 2019.


Publications
Publications

Parallel Hierarchical Composition Conflict-Based Search, Hannah Lee, James Motes, Marco Morales, Nancy M. Amato, IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol: 6, Issue: 4, pp. 7001-7008, Prague, Czech Republic, Jul 2021. DOI: 10.1109/LRA.2021.3096476.
Keywords: Multi-Agent, Parallel Algorithms, Path Planning
Links : [Published]

BibTex

@article{lee2021parallel,
title={Parallel Hierarchical Composition Conflict-Based Search for Optimal Multi-Agent Pathfinding},
author={Lee, Hannah and Motes, James and Morales, Marco and Amato, Nancy M},
journal={IEEE Robotics and Automation Letters},
volume={6},
number={4},
pages={7001--7008},
year={2021},
publisher={IEEE}
}


Abstract

In this letter, we present the following optimal multi-agent pathfinding (MAPF) algorithms: Hierarchical Composition Conflict-Based Search, Parallel Hierarchical Composition Conflict-Based Search, and Dynamic Parallel Hierarchical Composition Conflict-Based Search. MAPF is the task of finding an optimal set of valid path plans for a set of agents such that no agents collide with present obstacles or each other. The presented algorithms are an extension of Conflict-Based Search (CBS), where the MAPF problem is solved by composing and merging smaller, more easily manageable subproblems. Using the information from these subproblems, the presented algorithms can more efficiently find an optimal solution. Our three presented algorithms demonstrate improved performance for optimally solving MAPF problems consisting of many agents in crowded areas while examining fewer states when compared with CBS and its variant Improved Conflict-Based Search.


Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Search, Irving Solis, James Motes, Read Sandström, Nancy M. Amato, IEEE Robotics and Automation Letters, Mar 2021. DOI: NA
Keywords: Motion Planning, Multi-Agent
Links :

BibTex

@article{solis2019representation,
title={Representation-optimal multi-robot motion planning using conflict-based search},
author={Solis, Irving and Sandstr{\"o}m, Read and Motes, James and Amato, Nancy M},
journal={arXiv preprint arXiv:1909.13352},
year={2019}
}


Abstract

Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents each with individual start and goal states within a continuous state space. Existing approaches can be split into coupled methods which provide optimal solutions but struggle with scalability or decoupled methods which provide scalable solutions but offer no optimality guarantees. Recent work has explored hybrid approaches that leverage the advantages of both coupled and decoupled approaches in an easier discrete subproblem, Multi-Agent Pathfinding (MAPF). In this work, we adapt recent developments in hybrid MAPF to the continuous domain of MAMP. We demonstrate the scalability of our method to manage groups of up to 32 agents, demonstrate the ability to handle up to 8 high-DOF manipulators, and plan for heterogeneous teams. In all scenarios, our approach plans significantly faster while providing higher quality solutions.


Multi-Robot Task and Motion Planning with Subtask Dependencies, James Motes, Read Sandstrom, Hannah Lee, Shawna Thomas, Nancy M. Amato, IEEE Robotics and Automation Letters (RA-L), Vol: 5, Issue: 2, pp. 3338-3345, Feb 2020. DOI: 10.1109/LRA.2020.2976329
Keywords: Motion Planning, Multi-Agent, Task Planning
Links : [Published]

BibTex

@article{motes2020multi,
title={Multi-Robot Task and Motion Planning With Subtask Dependencies},
author={Motes, James and Sandstr{\"o}m, Read and Lee, Hannah and Thomas, Shawna and Amato, Nancy M},
journal={IEEE Robotics and Automation Letters},
volume={5},
number={2},
pages={3338--3345},
year={2020},
publisher={IEEE}
}


Abstract

We present a multi-robot integrated task and motion method capable of handling sequential subtask dependencies within multiply decomposable tasks. We map the multi-robot pathfinding method, Conflict Based Search, to task planning and integrate this with motion planning to create TMP-CBS. TMP-CBS couples task decomposition, allocation, and planning to support cases where the optimal solution depends on robot availability and inter-team conflict avoidance. We show improved planning time for simpler task sets and generate optimal solutions w.r.t. the state space representation for a broader range of problems than prior methods.


Interaction Templates for Multi-Robot Systems, James Motes, Read Sandstrom, Will Adams, Tobi Ogunyale, Shawna Thomas, Nancy M. Amato, IEEE Robotics and Automation Letters, Vol: 4, Issue: 3, pp. 2926-2933, Jun 2019. DOI: 10.1109/LRA.2019.2923386
Keywords: Interaction, Multi-Agent, Task Planning
Links : [Published]

BibTex

@article{motes2019interaction,
title={Interaction templates for multi-robot systems},
author={Motes, James and Sandstr{\"o}m, Read and Adams, Will and Ogunyale, Tobi and Thomas, Shawna and Amato, Nancy M},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={3},
pages={2926--2933},
year={2019},
publisher={IEEE}
}


Abstract

This letter describes a framework for multi-robot problems that require or utilize interactions between robots. Solutions consider interactions on a motion planning level to determine the feasibility and cost of the multi-robot team solution. Modeling these problems with current integrated task and motion planning (TMP) approaches typically requires reasoning about the possible interactions and checking many of the possible robot combinations when searching for a solution. We present a multi-robot planning method called Interaction Templates (ITs), which moves certain types of robot interactions from the task planner to the motion planner. ITs model interactions between a set of robots with a small roadmap. This roadmap is then tiled into the environment and connected to the robots’ individual roadmaps. The resulting combined roadmap allows interactions to be considered by the motion planner. We apply ITs to homogeneous and heterogeneous robot teams under both required and optional cooperation scenarios, which previously required a task planning method. We show improved performance over a current TMP planning approach.