Zobrazeno 1 - 10
of 663
pro vyhledávání: '"D'Amato, A. M. A."'
Multi-robot systems enhance efficiency and productivity across various applications, from manufacturing to surveillance. While single-robot motion planning has improved by using databases of prior solutions, extending this approach to multi-robot mot
Externí odkaz:
http://arxiv.org/abs/2411.08851
Autor:
Ngui, Isaac, McBeth, Courtney, He, Grace, Santos, André Corrêa, Soares, Luciano, Morales, Marco, Amato, Nancy M.
Many real-world tasks are intuitive for a human to perform, but difficult to encode algorithmically when utilizing a robot to perform the tasks. In these scenarios, robotic systems can benefit from expert demonstrations to learn how to perform each t
Externí odkaz:
http://arxiv.org/abs/2409.12862
Autor:
Elimelech, Khen, Motes, James, Morales, Marco, Amato, Nancy M., Vardi, Moshe Y., Kavraki, Lydia E.
Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. The complexity of such problems scales exponentially with the number of robots and task complexity, making them challenging fo
Externí odkaz:
http://arxiv.org/abs/2409.10692
Autor:
Ashur, Stav, Lusardi, Maria, Markowicz, Marta, Motes, James, Morales, Marco, Har-Peled, Sariel, Amato, Nancy M.
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 algorithmic methods such as probabilistic roadmaps less viable, as nod
Externí odkaz:
http://arxiv.org/abs/2407.00259
Autor:
Attali, Amnon, Ashur, Stav, Love, Isaac Burton, McBeth, Courtney, Motes, James, Morales, Marco, Amato, Nancy M.
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 bias their sampling using various heuristics relat
Externí odkaz:
http://arxiv.org/abs/2404.03133
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 conflicts. ARC creates subproblems centered around conflicts, and the solution
Externí odkaz:
http://arxiv.org/abs/2312.08554
In this work, we present a multi-robot planning framework that leverages guidance about the problem to efficiently search the planning space. This guidance captures when coordination between robots is necessary, allowing us to decompose the intractab
Externí odkaz:
http://arxiv.org/abs/2311.10176
Autor:
Uwacu, Diane, Yammanuru, Ananya, Nallamotu, Keerthana, Chalasani, Vasu, Morales, Marco, Amato, Nancy M.
We present a hierarchical tree-based motion planning strategy, HAS-RRT, guided by the workspace skeleton to solve motion planning problems in robotics and computational biology. Relying on the information about the connectivity of the workspace and t
Externí odkaz:
http://arxiv.org/abs/2309.10801
Autor:
Attali, Amnon, Ashur, Stav, Love, Isaac Burton, McBeth, Courtney, Motes, James, Uwacu, Diane, Morales, Marco, Amato, Nancy M.
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 bia
Externí odkaz:
http://arxiv.org/abs/2210.08640
Publikováno v:
IEEE Robotics and Automation Letters, vol. 8, no. 11, pp. 6867-6874, Nov. 2023
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
Externí odkaz:
http://arxiv.org/abs/2210.07141