An Adaptive and Energy-Aware Path Planning Strategy (AEPPS) for ASVs with Obstacle Avoidance
Linze Liu, Princy Johnson, Daniel Gutiérrez Reina · 2025
The proliferation of debris poses an increasing threat to aquatic ecosystems, necessitating scalable autonomous solutions for effective environmental remediation. This research proposes a novel hierarchical path planning framework for Autonomous Surface Vehicles (ASVs) that significantly improves energy efficiency and navigational capabilities while ensuring real-time operability in resource-constrained Autonomous Surface Vehicles (ASVs). The proposed AEPPS architecture integrates three complementary algorithms across distinct temporal scales: an energy-aware A* algorithm for global route optimization (1Hz), a biased Rapidly-Exploring Random Tree (RRT) for local obstacle negotiation (5Hz), and a Model Predictive Control (MPC) framework for trajectory refinement (10Hz). Key methodological innovations include hydrodynamic force-informed heuristics, environmental-biased sampling distributions, and adaptive prediction horizons responsive to dynamic aquatic conditions. Rigorous validation through ROS2/Gazebo simulations demonstrates statistically significant performance improvements (p%) under highly dynamic conditions. Computational analysis confirms feasibility for deployment on embedded ASV systems with stringent power constraints.