UP-Bench: A Benchmark for Underwater Path Planning Algorithms

Path planning is a critical challenge for Autonomous Underwater Vehicles (AUVs) due to complex underwater environments, including ocean currents, dynamic obstacles, and limited sensing capabilities. The lack of a standardized benchmarking framework has hindered direct comparisons between algorithms, slowing progress in the field. To address this, we introduce an open-source benchmarking platform for underwater AUV path planning, designed to provide a unified evaluation environment, automated performance assessment, and reproducible experiments. Built on the HoloOcean simulation platform, our benchmark incorporates realistic underwater dynamics, such as ocean currents, static and dynamic obstacles, and sensor models. It supports a range of path planning tasks, from basic obstacle avoidance to complex scenarios with current disturbances. The platform is compatible with classical algorithms (e.g., A*, RRT), evolutionary methods (e.g., GA, ACO), and deep reinforcement learning (e.g., Soft Actor-Critic, SAC). We define key evaluation metrics, including path efficiency (length, smoothness, energy consumption), task success rate, collision rate, and computational cost. Automated tools enable systematic algorithm comparisons across scenarios, generating standardized performance results and visualizations. This open-source, extensible framework aims to advance underwater path planning research by enabling fair comparisons and guiding future algorithm development. It provides a scalable foundation for evaluating AUV path planning methods under simulated real-world conditions, fostering innovation in the field. All source code and experimental configurations will be available on the GitHub Page: https://github.com/IoET-y/UP-bench.