mlsanityData to 7 Oct 2026

A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles

arXiv 2301.06863 ↗First posted 17 Jan 2023cs.RO cs.LG cs.SYcited by 0

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Abstract (arXiv)

Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex methods, such as long baseline and ultra-short baseline systems. Nonetheless, in ROSB target localization methods, the trajectory of the tracking vehicle near the localized target plays an important role in obtaining the best accuracy of the predicted target position. Here, we investigate a Reinforcement Learning (RL) approach to find the optimal path that an autonomous vehicle should follow in order to increase and optimize the overall accuracy of the predicted target localization, while reducing time and power consumption. To accomplish this objective, different experimental tests have been designed using state-of-the-art deep RL algorithms. Our study also compares the results obtained with the analytical Fisher information matrix approach used in previous studies. The results revealed that the policy learned by the RL agent outperforms trajectories based on these analytical solutions, e.g. the median predicted error at the beginning of the target's localisation is 17% less. These findings suggest that using deep RL for localizing acoustic targets could be successfully applied to in-water applications that include tracking of acoustically tagged marine animals by autonomous underwater vehicles. This is envisioned as a first necessary step to validate the use of RL to tackle such problems, which could be used later on in a more complex scenarios

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 41 references that are in the map. First posted on arXiv 17 Jan 2023.

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10 of 41 references.

References · 10

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  1. 2025Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
  2. 2020Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles
  3. 2026Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach
  4. 2024Is FISHER All You Need in The Multi-AUV Underwater Target Tracking Task?
  5. 2026Diffusion-Guided Cooperative Policy Learning for Target Tracking Based on Underwater Mobile Agent Networks
  6. 2025Ocean Diviner: A Diffusion-Augmented Reinforcement Learning Framework for AUV Robust Control in Underwater Tasks ↗
  7. 2026Audio-based UAV Localization with Adaptive Temporal Correspondence via Reinforcement Learning ↗
  8. 2026Sim-to-reality adaptation for Deep Reinforcement Learning applied to an underwater docking application