RENEW: Risk- and Energy-Aware Navigation in Dynamic Waterways
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909998831894528 |
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| author | Jeong, Mingi Li, Alberto Quattrini |
| author_facet | Jeong, Mingi Li, Alberto Quattrini |
| contents | We present RENEW, a global path planner for Autonomous Surface Vehicle (ASV) in dynamic environments with external disturbances (e.g., water currents). RENEW introduces a unified risk- and energy-aware strategy that ensures safety by dynamically identifying non-navigable regions and enforcing adaptive safety constraints. Inspired by maritime contingency planning, it employs a best-effort strategy to maintain control under adverse conditions. The hierarchical architecture combines high-level constrained triangulation for topological diversity with low-level trajectory optimization within safe corridors. Validated with real-world ocean data, RENEW is the first framework to jointly address adaptive non-navigability and topological path diversity for robust maritime navigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16424 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RENEW: Risk- and Energy-Aware Navigation in Dynamic Waterways Jeong, Mingi Li, Alberto Quattrini Robotics Artificial Intelligence We present RENEW, a global path planner for Autonomous Surface Vehicle (ASV) in dynamic environments with external disturbances (e.g., water currents). RENEW introduces a unified risk- and energy-aware strategy that ensures safety by dynamically identifying non-navigable regions and enforcing adaptive safety constraints. Inspired by maritime contingency planning, it employs a best-effort strategy to maintain control under adverse conditions. The hierarchical architecture combines high-level constrained triangulation for topological diversity with low-level trajectory optimization within safe corridors. Validated with real-world ocean data, RENEW is the first framework to jointly address adaptive non-navigability and topological path diversity for robust maritime navigation. |
| title | RENEW: Risk- and Energy-Aware Navigation in Dynamic Waterways |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2601.16424 |