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This research advances active sonar perception by combining CNN-based likelihood estimation with reinforcement learning (PPO) to optimize viewpoint selection in real-time, effectively bypassing computationally heavy online planning. The hybrid approach reduces motion costs and sensing steps while maintaining high recognition accuracy for marine debris detection without requiring annotated angle data.
A Hybrid Model-Based and Model-Free Framework for Active Multi-View Viewpoint Optimization in Sonar Target Recognition
arXiv:2606.15373v1 Announce Type: new Abstract: This paper presents a hybrid model-based and model-free framework for active multi-view target recognition using forward-looking sonar. A convolutional neural network (CNN) provides data-driven observation likelihoods, while Radon-based orientation estimation enables viewpoint-aware sensing without requiring angle annotations. During training, an information-gain-based reward guides a Proximal Policy Optimization (PPO) agent to learn a belief-aware viewpoint selection policy offline. At deployment, the learned policy performs real-time viewpoint selection using only CNN-based belief updates, eliminating the need for computationally expensive online POMDP tree search. Experiments on a marine-debris forward-looking sonar dataset demonstrate that the proposed approach achieves competitive recognition accuracy while reducing sensing steps and motion cost compared to model-based baselines.
Abstract:This paper presents a hybrid model-based and model-free framework for active multi-view target recognition using forward-looking sonar. A convolutional neural network (CNN) provides data-driven observation likelihoods, while Radon-based orientation estimation enables viewpoint-aware sensing without requiring angle annotations. During training, an information-gain-based reward guides a Proximal Policy Optimization (PPO) agent to learn a belief-aware viewpoint selection policy offline. At deployment, the learned policy performs real-time viewpoint selection using only CNN-based belief updates, eliminating the need for computationally expensive online POMDP tree search. Experiments on a marine-debris forward-looking sonar dataset demonstrate that the proposed approach achieves competitive recognition accuracy while reducing sensing steps and motion cost compared to model-based baselines.
Robotics (cs.RO)
arXiv:2606.15373 [cs.RO]
(or arXiv:2606.15373v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.15373 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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