43 citations · 72 across the 15 of their papers we have counts for
15 papers
SPITE: Simple Polyhedral Intersection Techniques for modified Environments
Stav Ashur, Maria Lusardi, Marta Markowicz +4
Motion planning in modified environments is a challenging task, as it compounds the innate difficulty of the motion planning problem with a changing environment. This renders some…
A Framework for Guided Motion Planning
Amnon Attali, Stav Ashur, Isaac Burton Love +4
Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem insta…
Adaptive Robot Coordination: A Subproblem-based Approach for Hybrid Multi-Robot Motion Planning
Irving Solis, James Motes, Mike Qin +2
This work presents Adaptive Robot Coordination (ARC), a novel hybrid framework for multi-robot motion planning (MRMP) that employs local subproblems to resolve inter-robot conflict…
Scalable Multi-Robot Motion Planning Using Workspace Guidance-Informed Hypergraphs
Courtney McBeth, James Motes, Isaac Ngui +2
In this work, we propose a method for multiple mobile robot motion planning that efficiently plans for robot teams up to 128 robots (an order of magnitude larger than existing stat…
HAS-RRT: RRT-based Motion Planning using Topological Guidance
Diane Uwacu, Ananya Yammanuru, Keerthana Nallamotu +3
We present a hierarchical RRT-based motion planning strategy, Hierarchical Annotated-Skeleton Guided RRT (HAS-RRT), guided by a workspace skeleton, to solve motion planning problem…
Evaluating Guiding Spaces for Motion Planning
Amnon Attali, Stav Ashur, Isaac Burton Love +5
Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem insta…
Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance
Courtney McBeth, James Motes, Diane Uwacu +2
Multi-robot motion planning (MRMP) is the problem of finding collision-free paths for a set of robots in a continuous state space. The difficulty of MRMP increases with the number…
Hypergraph-based Multi-Robot Task and Motion Planning
James Motes, Tan Chen, Timothy Bretl +2
We present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, results in solution times up to three orders of magnitu…
Hierarchical Planning with Annotated Skeleton Guidance
Diane Uwacu, Ananya Yammanuru, Marco Morales +1
We present a hierarchical skeleton-guided motion planning algorithm to guide mobile robots. A good skeleton maps the connectivity of the subspace of c-space containing significant…
Discrete State-Action Abstraction via the Successor Representation
Amnon Attali, Pedro Cisneros-Velarde, Marco Morales +1
While the difficulty of reinforcement learning problems is typically related to the complexity of their state spaces, Abstraction proposes that solutions often lie in simpler under…
Insights from an Industrial Collaborative Assembly Project: Lessons in Research and Collaboration
Tan Chen, Zhe Huang, James Motes +13
Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technol…
Annotated-skeleton Biased Motion Planning for Faster Relevant Region Discovery
Diane Uwacu, Regina Rex, Bonnie Wang +2
Motion planning algorithms often leverage topological information about the environment to improve planner performance. However, these methods often focus only on the environment's…
Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Search
Irving Solis, Read Sandström, James Motes +1
Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents given individual start and goal states. Given the hardness of MAMP, most of the re…
Provably Optimal Parallel Transport Sweeps on Semi-Structured Grids
Michael P. Adams, Marvin L. Adams, W. Daryl Hawkins +7
We have found provably optimal algorithms for full-domain discrete-ordinate transport sweeps on a class of grids in 2D and 3D Cartesian geometry that are regular at a coarse level…
SLAP: Simultaneous Localization and Planning Under Uncertainty for Physical Mobile Robots via Dynamic Replanning in Belief Space: Extended version
Ali-akbar Agha-mohammadi, Saurav Agarwal, Sung-Kyun Kim +2
Simultaneous localization and Planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous POMDP (p…