Hyperparameter optimization for PSO-based energy-aware path planning for AUV swarms

In missions using autonomous underwater vehicles (AUVs), reaching predefined waypoints is essential, e.g., for seabed mapping and infrastructure monitoring. The primary objective is to minimize energy consumption to reduce the risk of failure, avoid long (re)charging times, or maximize the number of successfully visited waypoints. If the AUVs can visit the waypoints in any order, we can frame this as a Travelling Salesperson Problem (TSP). However, finding the global optimal solution to the TSP becomes computationally infeasible even for relatively small sets of waypoints. To address this challenge, we utilize Particle Swarm Optimization (PSO) as a lightweight alternative, allowing us to obtain energy-efficient routes that closely approximate the global solution. Choosing the corresponding hyperparameters is fundamental, as poor selections can lead to local optima. Our analysis shows significant differences in the sensitivity of hyperparameters between the two PSO-based approaches, which only differ in initialization. However, there is a consistent range of hyperparameters where both methods yield comparable results. We identify this range by optimizing hyperparameters to improve solution quality. Simulative evaluations in real-world-inspired scenarios demonstrate that optimized hyperparameter selection improves the energy efficiency of the AUV swarm and ensures reliable mission execution using a minimal number of AUVs.

DDC Class

629.892: Robot

519: Applied Mathematics, Probabilities

Funding Organisations

Hamburg University of Technology

Lizenz

http://rightsstatements.org/vocab/InC/1.0/

Name

arcs25_frenkel.pdf

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1.28 MB

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Adobe PDF

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Hyperparameter optimization for PSO-based energy-aware path planning for AUV swarms