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

Mingi Jeong, Alberto Quattrini Li

TL;DR

This work tackles robust ASV navigation in dynamic waterways by introducing RENEW, a hierarchical global planner that jointly considers safety under external disturbances and energy efficiency. It uses constrained Delaunay triangulation to generate topologically distinct channels, adaptive padding to create risk-aware safe corridors, and a best-effort contingency framework to guarantee safe maneuvers under worst-case disturbances. After identifying multiple homotopic options, it selects the optimal channel via a harmonic-mean fuel-cost criterion and refines the trajectory within that channel to minimize energy use. Validations on real current data and diverse environments show improved fuel efficiency and safety, with robust performance under contingency scenarios and adaptive routing across seasonal currents. The approach promises practical impact for maritime autonomy by providing topology-aware, risk-aware, energy-efficient navigation in complex, disturbance-prone waterways.

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.

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

TL;DR

This work tackles robust ASV navigation in dynamic waterways by introducing RENEW, a hierarchical global planner that jointly considers safety under external disturbances and energy efficiency. It uses constrained Delaunay triangulation to generate topologically distinct channels, adaptive padding to create risk-aware safe corridors, and a best-effort contingency framework to guarantee safe maneuvers under worst-case disturbances. After identifying multiple homotopic options, it selects the optimal channel via a harmonic-mean fuel-cost criterion and refines the trajectory within that channel to minimize energy use. Validations on real current data and diverse environments show improved fuel efficiency and safety, with robust performance under contingency scenarios and adaptive routing across seasonal currents. The approach promises practical impact for maritime autonomy by providing topology-aware, risk-aware, energy-efficient navigation in complex, disturbance-prone waterways.

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.
Paper Structure (16 sections, 6 equations, 10 figures, 4 tables)

This paper contains 16 sections, 6 equations, 10 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Problem Formulation
  4. Main Approaches
  5. No Go Zone
  6. Homotopic Channel
  7. Adaptive No Go Zone
  8. High-level Planner
  9. Low-level Planner
  10. Experiments and Results
  11. Comparative Studies
  12. Real-world Environment
  13. Ablation Studies
  14. Contingency Maneuver
  15. Conclusion and Future Steps
  16. ...and 1 more sections

Figures (10)

  • Figure 1: Path planning under external disturbances. (top) Our method selects energy-efficient paths across multiple homotopy classes, using current-based adaptive padding (gray) around obstacles (brown), to ensure feasible contingency maneuvers. (bottom) The baseline prioritizes distance over energy/homotopy. Without adaptive padding, its paths lack safety margins for contingency maneuvers.
  • Figure 2: System Architecture.
  • Figure 3: CDF-based navigation mesh and a single CDT homotopic channel. The color is lighter from the start (10,10) to the goal (90,90). Hard No Go Zone $\mathcal{L}$ is brown.
  • Figure 4: Irregular turning circle behaviors under the external disturbances with their noises: (top) northward current; (bottom) southward current; (left) turning circles from the robot in the example at (0,0) with heading samples $[\phi-\Delta\phi, \phi+\Delta\phi]$. The closest turning circle (magenta) and its closest point (black dot) to the constrained edge are marked; (mid) turning circle behaviors under disturbance noises; (right) offset padding $\mathcal{V}(\tau)$ for the constrained edge (gray), to ensure safety within the probabilistic bound.
  • Figure 5: Probabilistic distribution and bounds of the best-effort maneuvers. (left) spatial extent of extreme points induced by best-effort actions; and (right) cumulative distribution of best-effort distances, along with bounded values based on the 95th percentile threshold ($\sigma=0.95$ for 4.47m: northward current, 1.96m: southward current).
  • ...and 5 more figures

Theorems & Definitions (1)

  • Definition 1