A Framework for Guided Motion Planning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914966199599104 |
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| author | Attali, Amnon Ashur, Stav Love, Isaac Burton McBeth, Courtney Motes, James Morales, Marco Amato, Nancy M. |
| author_facet | Attali, Amnon Ashur, Stav Love, Isaac Burton McBeth, Courtney Motes, James Morales, Marco Amato, Nancy M. |
| contents | 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 related to the known underlying structure of the search space. In this work, we formalize the intuitive notion of guided search by defining the concept of a guiding space. This new language encapsulates many seemingly distinct prior methods under the same framework, and allows us to reason about guidance, a previously obscured core contribution of different algorithms. We suggest an information theoretic method to evaluate guidance, which experimentally matches intuition when tested on known algorithms in a variety of environments. The language and evaluation of guidance suggests improvements to existing methods, and allows for simple hybrid algorithms that combine guidance from multiple sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03133 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Framework for Guided Motion Planning Attali, Amnon Ashur, Stav Love, Isaac Burton McBeth, Courtney Motes, James Morales, Marco Amato, Nancy M. Robotics Artificial Intelligence 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 related to the known underlying structure of the search space. In this work, we formalize the intuitive notion of guided search by defining the concept of a guiding space. This new language encapsulates many seemingly distinct prior methods under the same framework, and allows us to reason about guidance, a previously obscured core contribution of different algorithms. We suggest an information theoretic method to evaluate guidance, which experimentally matches intuition when tested on known algorithms in a variety of environments. The language and evaluation of guidance suggests improvements to existing methods, and allows for simple hybrid algorithms that combine guidance from multiple sources. |
| title | A Framework for Guided Motion Planning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2404.03133 |