Towards Energy-Aware Path Planning for AUV Swarms

Autonomous underwater vehicles (AUVs) are being researched for maritime applications such as ocean exploration and underwater maintenance. A swarm of AUVs is used for complex missions, with each AUV given a specific part of the mission. Energy-aware path planning is crucial for increasing operation time and reliability. This article discusses different path planning strategies for AUV swarms, comparing a particle swarm optimizer (PSO) and a greedy approach to determine the most energy-efficient route. One challenge here is to divide the total number of waypoints among the AUVs reasonably. For this purpose, we investigate a k-means algorithm and an extended variant of the PSO, in which we distribute the clusters dynamic according to a balanced metric. The aim is to find an algorithm that can be used for different applications and validated by different test scenarios focussing on energy consumption and mission fulfillment.