arXiv subjects

James D. Motes

Publications and source records attributed to James D. Motes.

7 recordsLinked to original sources

arXiv2026

P-ARC: Exploiting Subproblem Independence for Parallel Multi-Robot Motion Planning

This paper presents Parallel ARC (P-ARC), a parallel formulation of the Adaptive Robot Coordination (ARC) approach to multi-robot motion planning (MRMP) which exploits subproblem independence. ARC's adaptive (de)composition of the multi-robot planning space exposes parallelism: single-robot paths are solved independently and iterative conflict detection and resolution create locally coupled subproblems. While distributing single-robot queries is trivial, not all conflicts are independent, so P-ARC proposes robot-disjoint conflict batches which enable efficient distributed detection and concurrent repair. Additionally, OR-multi-start strategies are employed at the global and subproblem resolution levels, creating a hybrid parallel strategy OR-P-ARC. We evaluate the methods against sequential ARC, multi-start OR-ARC, and coupled and prioritized parallel baselines on controlled 2D mobile robot and planar-manipulator problems with up to 256 robots and 3D Panda manipulator problems with up to 16 robots. On 16-robot Panda tasks, with 16 workers, P-ARC and OR-P-ARC achieve 3.48X and 6.67X speedups, respectively.

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arXiv2026

AO-ARC: Almost-Surely Asymptotically Optimal Multi-Robot Motion Planning with ARC

We present AO-ARC, an anytime multi-robot motion planning (MRMP) method that achieves initial solution times on par with state-of-the-art MRMP feasibility solvers while converging faster and more reliably than existing anytime MRMP methods as the number of robots increases. AO-ARC adapts the AO-x meta-algorithm for converting feasibility solvers into anytime algorithms by iteratively calling the original ARC method on bounded MRMP instances under a makespan cost metric. This exploits the adaptive (de)coupling of ARC while maintaining the consistent cost bound across robot (de)compositions needed for AO-x. We provide theoretical analysis proving the asymptotic optimality properties of AO- ARC and conduct empirical evaluation on a set of 2D scenarios with different levels of coordination complexity and a 3D manipulator scenario representative of real-world applications.

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arXiv2026

Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement

A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large computational expense of searching the joint configuration space of the robot group. In this work, we present a method for multiple mobile robot motion planning that achieves an improvement in planning time up to an order of magnitude by leveraging the insight that we can use discrete search over a workspace decomposition to provide coordination between robots during planning. While prior work uses workspace topology to inform when coordination between robots is needed and then composes robots into their joint configuration space, we take a step further by iteratively refining our workspace representation to allow our planner to search smaller, decoupled configuration spaces.

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arXiv2026

Multi-Robot Motions in Milliseconds: Vector-Accelerated Primitives for Sampling-Based Planning

In this paper, we extend the recent Vector-Accelerated Motion Planning (VAMP) framework to multi-robot motion planning. We develop two vector-accelerated primitives, multi-robot MotionValidation (MotVal) and FindFirstConflict (FFC), which exploit SIMD parallelism within the multi-robot domain. On pure multi-robot motion validation tests, this achieves over 1415X speedup in validation time. Additionally, we modify a representative set of algorithms to use these new primitives. We evaluate five vector-accelerated multi-robot motion planning (VA-MRMP) algorithms on manipulator, 2D mobile robot, and heterogeneous teams, observing planning speedups over FCL of up to 1492X. With VA-MRMP, all five planners attain subsecond median runtimes on problems with four Panda manipulators.

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arXiv2026

Serialized Red-Green-Gray: Quicker Heuristic Validation of Edges in Dynamic Roadmap Graphs

Motion planning in dynamic environments, such as robotic warehouses, requires fast adaptation to frequent changes in obstacle poses. Traditional roadmap-based methods struggle in such settings, relying on inefficient reconstruction of a roadmap or expensive collision detection to update the existing roadmap. To address these challenges we introduce the Red-Green-Gray (RGG) framework, a method that builds on SPITE to quickly classify roadmap edges as invalid (red), valid (green), or uncertain (gray) using conservative geometric approximations. Serial RGG provides a high-performance variant leveraging batch serialization and vectorization to enable efficient GPU acceleration. Empirical results demonstrate that while RGG effectively reduces the number of unknown edges requiring full validation, SerRGG achieves a 2-9x speedup compared to the sequential implementation. This combination of geometric precision and computational speed makes SerRGG highly effective for time-critical robotic applications.

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arXiv2025

An Analysis of Constraint-Based Multi-Agent Pathfinding Algorithms

This study informs the design of future multi-agent pathfinding (MAPF) and multi-robot motion planning (MRMP) algorithms by guiding choices based on constraint classification for constraint-based search algorithms. We categorize constraints as conservative or aggressive and provide insights into their search behavior, focusing specifically on vanilla Conflict-Based Search (CBS) and Conflict-Based Search with Priorities (CBSw/P). Under a hybrid grid-roadmap representation with varying resolution, we observe that aggressive (priority constraint) formulations tend to solve more instances as agent count or resolution increases, whereas conservative (motion constraint) formulations yield stronger solution quality when both succeed. Findings are synthesized in a decision flowchart, aiding users in selecting suitable constraints. Recommendations extend to Multi-Robot Motion Planning (MRMP), emphasizing the importance of considering topological features alongside problem, solution, and representation features. A comprehensive exploration of the study, including raw data and map performance, is available in our public GitHub Repository: https://GitHub.com/hannahjmlee/constraint-mapf-analysis

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arXiv2025

ERUPT: An Open Toolkit for Interfacing with Robot Motion Planners in Extended Reality

We propose the Extended Reality Universal Planning Toolkit (ERUPT), an extended reality (XR) system for interactive motion planning. Our system allows users to create and dynamically reconfigure environments while they plan robot paths. In immersive three-dimensional XR environments, users gain a greater spatial understanding. XR also unlocks a broader range of natural interaction capabilities, allowing users to grab and adjust objects in the environment similarly to the real world, rather than using a mouse and keyboard with the scene projected onto a two-dimensional computer screen. Our system integrates with MoveIt, a manipulation planning framework, allowing users to send motion planning requests and visualize the resulting robot paths in virtual or augmented reality. We provide a broad range of interaction modalities, allowing users to modify objects in the environment and interact with a virtual robot. Our system allows operators to visualize robot motions, ensuring desired behavior as it moves throughout the environment, without risk of collisions within a virtual space, and to then deploy planned paths on physical robots in the real world.

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