Path Planning Algorithm Comparison Analysis for Wireless AUVs Energy Sharing System

TL;DR

The paper addresses enabling wireless energy sharing among AUVs by integrating path planning to route energy-transfer vehicles through obstacle-rich underwater environments. It compares two trajectory-planning approaches, RRT* (asymptotically optimal) and PSO (population-based), to generate feasible, energy-aware trajectories and evaluates them in MATLAB under random and irregular obstacle scenarios. Results show that RRT* achieves shorter and more consistent path costs $C(\tau)$ (average around $77.64$ with std $13.07$) while PSO yields longer, more variable paths (average around $97.78$ with std $52.61$); both are evaluated within the free space $X_{free}$ of the underwater workspace $X \subset \mathbb{R}^3$ and subject to obstacle constraints. The findings inform practical design of energy-sharing AUV systems and highlight the trade-offs between optimality and computational efficiency for underwater path planning.

Abstract

Autonomous underwater vehicles (AUVs) are increasingly used in marine research, military applications, and undersea exploration. However, their operational range is significantly affected by battery performance. In this paper, a framework for a wireless energy sharing system among AUVs is proposed, enabling rapid energy replenishment. Path planning plays a crucial role in the energy-sharing process and autonomous navigation, as it must generate feasible trajectories toward designated goals. This article focuses on efficient obstacle avoidance in complex underwater environments, including irregularly shaped obstacles and narrow passages. The proposed method combines Rapidly-exploring Random Trees Star (RRT*) with Particle Swarm Optimization (PSO) to improve path planning efficiency. Comparative analysis of the two algorithms is presented through simulation results in both random and irregular obstacle environments. Index Terms: Wireless charging, autonomous underwater vehicles (AUVs), path planning, irregular obstacles, narrow passages, RRT*, particle swarm optimization (PSO).

Figures (8)

• Figure 1: The Framework of Wireless AUVs Energy Sharing System
• Figure 2: The Model of AUVs' Trajectory in RRT*
• Figure 3: The Model of AUVs' Trajectory in PSO
• Figure 4: The Flow Chart of PSO
• Figure 5: The Path Result of Two Algorithms in Random Obstacles