Feedback-Based Mobile Robot Navigation in 3-D Environments Using Artificial Potential Functions Technical Report
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911375982329856 |
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| author | Lang, Ro'i Rimon, Elon |
| author_facet | Lang, Ro'i Rimon, Elon |
| contents | This technical report presents the construction and analysis of polynomial navigation functions for motion planning in 3-D workspaces populated by spherical and cylindrical obstacles. The workspace is modeled as a bounded spherical region, and obstacles are encoded using smooth polynomial implicit functions. We establish conditions under which the proposed navigation functions admit a unique non-degenerate minimum at the target while avoiding local minima, including in the presence of pairwise intersecting obstacles. Gradient and Hessian analyses are provided, and the theoretical results are validated through numerical simulations in obstacle rich 3-D environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09318 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Feedback-Based Mobile Robot Navigation in 3-D Environments Using Artificial Potential Functions Technical Report Lang, Ro'i Rimon, Elon Robotics Systems and Control This technical report presents the construction and analysis of polynomial navigation functions for motion planning in 3-D workspaces populated by spherical and cylindrical obstacles. The workspace is modeled as a bounded spherical region, and obstacles are encoded using smooth polynomial implicit functions. We establish conditions under which the proposed navigation functions admit a unique non-degenerate minimum at the target while avoiding local minima, including in the presence of pairwise intersecting obstacles. Gradient and Hessian analyses are provided, and the theoretical results are validated through numerical simulations in obstacle rich 3-D environments. |
| title | Feedback-Based Mobile Robot Navigation in 3-D Environments Using Artificial Potential Functions Technical Report |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2601.09318 |