James Motes's research while affiliated with University of Illinois, Urbana-Champaign and other places
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Publications (11)
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 of robots and is exacerbated in environments with narrow passages that robots must pass through, like warehouse aisles where coordination between robots is required. In s...
In this article, 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 magnitude faster than the existing methods and successfully plans for problems with up to 20 objects, more than three times as many objects as comparable method...
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 instances. Most variants do not sample uniformly at random, and instead bias their sampling using various heuristics for determining which samples will provide more informatio...
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 of robots due to the increased potential for collisions between robots. This problem is exacerbated in environments with narrow passages that robots must pass through, li...
We present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, produces solution times up to three orders of magnitude faster than existing methods. We achieve this improvement by decomposing the planning space into subspaces for independent manipulators, objects, and manipulators holdin...
Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. This paper presents lessons from a collaborative assembly project between three academic research grou...
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 s...
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 of...
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...
Multi-Agent Pathfinding (MAPF) is the problem of finding a set of feasible paths for a set of agents with specific individual start and goal poses. It is considered computationally hard to solve. Conflict-based search (CBS) has shown optimality in developing solutions for multi-agent pathfinding problems in discrete spaces. However, neither CBS nor...
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 require...
Citations
... Hartmann et al. [1] addressed assembly planning for multiple manipulators by decomposing the problem into sub-problems while considering constraints. Motes et al. [15] introduced a hypergraph representation for multi-robot task and motion planning, demonstrating its effectiveness in generic rearrangement tasks. Gao et al. [16] proposed a dependency graph-guided heuristic search procedure tailored to nonmonotone rearrangement tasks, albeit considering only two manipulators. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/272821716779029-1442057174663_Q64/Nancy-Amato.jpg)
... CBS has a two-layer framework construction, where the bo om algorithm completes the path planning of a single intelligent vehicle and the upper algorithm is responsible for detecting and resolving path conflicts. The authors of [23][24][25] improved the CBS algorithm's efficiency in finding solutions, but there is no assurance that the outcomes will be the best ones. ...
... Coupled methods (e.g., Composite RRT [2] and Composite PRM [1]) provide this coordination but consider a large search space that becomes computationally intractable for large teams. Hybrid methods (e.g., CBS-MP [3] and M* [4]) leverage the scalability of decoupled methods along with an increased level of coordination. However, in environments with narrow passages where inter-robot collisions are likely, their performance is limited by the time spent resolving conflicts. ...
... Some problems, such as payload transportation, model tasks abstractly and often ignore the physical dimensions of the tasks during planning [3], [4], [5]. Other problems, especially those involving object manipulation, must consider physical constraints [6], [7], [8], [9], [10]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/774112905551873-1561574307574_Q64/Shawna-Thomas-2.jpg)
... As such, they cannot be naturally extended to incorporate multi-robot semantics-task allocation or collision avoidance, and would have to treat the multi-robot system as a combined system, which becomes intractable as the number of robots increases. A multi-robot TMP approach in the context of transportation tasks is presented in [5]. They introduce Interaction Templates (IT) that enable handing over payloads from one robot to another. ...
