RENEW: Risk- and Energy-Aware Navigation in Dynamic Waterways

2026-01-23

AI Summary

This work proposes a risk- and energy-aware hierarchical navigation framework to address the challenge of jointly ensuring safety and energy efficiency in autonomous surface vessel path planning within dynamic maritime environments. The approach uniquely integrates adaptive no-go zone determination with topologically diverse path generation, employing constrained Delaunay triangulation to construct high-level path alternatives. At the low level, it embeds dynamic risk assessment and trajectory optimization within safe corridors, incorporating a best-effort control strategy inspired by maritime emergency response protocols. Experimental validation using real-world oceanographic data demonstrates that the framework reliably generates robust paths that are safe, energy-efficient, and topologically diverse, even under complex and varying current disturbances.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

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.

Problem

• Autonomous Surface Vehicle
• dynamic waterways
• risk-aware navigation
• energy-aware navigation
• non-navigable regions

Innovation

• risk-aware navigation
• energy-aware planning
• adaptive non-navigability
• topological path diversity
• autonomous surface vehicle