RENEW: Risk- and Energy-Aware Navigation in Dynamic Waterways
This addresses robust maritime navigation for autonomous surface vehicles, though it appears incremental as it builds on existing contingency planning and hierarchical architectures.
The paper tackles the problem of autonomous surface vehicle navigation in dynamic waterways with external disturbances by introducing RENEW, a global path planner that ensures safety through dynamic identification of non-navigable regions and adaptive constraints, validated with real-world ocean data.
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.