A mapless deep reinforcement learning (DRL) framework is proposed that fuses visual and 2D LiDAR data, integrates a self-attention soft actor-critic (SAC) architecture and a customized reward function, and achieves robust autonomous navigation and exploration without requiring obstacle priors.
This method formalizes the navigation task as a semiMarkov decision process and constructs a two-layer decision architecture with collaboration between a high-level manager and a low-level worker with collaboration between a high-level manager and a low-level worker.
Qi-Ming Chen· International Conference on...· 0 citations
Autonomous navigation in real-world public service, industrial inspection, and emergency response often faces frequent changes in nominally static scene structures, which can quickly invalidate pre-built global maps and naturally lead to a mapless navigation setting. We propose an end-to-end 3D LiDAR based navigation f...
Yue Zhai, Yan-Zi Miao· IEEE Robotics and Automation...· 0 citations
Obstacle avoidance in cluttered environments presents a significant challenge, as it requires rapid perception and decision-making in partially observable three-dimensional space, particularly for unmanned aerial vehicles (UAVs) operating with constrained onboard vision. Compared with traditional map-based approaches,...
Zi-Yin Meng, Jin-Biao Dong, Chuan-Gang Zhao· Measurement and control (Lon...· 0 citations
This paper proposes LSTP-Nav, a lightweight, decentralized navigation framework built on LSTP-Net that maps stacked 2D LiDAR observations, goal information, and velocity feedback directly to action and introduces an HS reward to provide smooth, heading-aware safety feedback, and develops PhysReplay-SimLab to improve tr...
Xingrong Diao, Zhi-Qiang Sun, Jian-Wei Peng et al.· IEEE Transactions on Automat...· 0 citations
. Because autonomous mobile robots operating in complex dynamic environments encounter several difficult problems, namely sudden dynamic obstacles, heterogeneous information from multiple sensors, and environmental uncertainty, this paper presents a thorough investigation of using deep learning and multi-sensor fusion...
Tian-Lin Guo· Proceedings of the 3rd Inter...· 0 citations
Mapless deep reinforcement learning (DRL) navigation in dynamic indoor environments is difficult under single-frame 2D LiDAR, which reports where an obstacle is but not whether it is approaching. We introduce the Positional-Velocity Spatio-Temporal Attention Module (PV-STAM), a compact perception block (19,968 trainabl...
Anas Mahyoub Naji Saeed Alqadhi, Munef El Muhammed, M. A. M. S. Bajhaw et al.· Applied Sciences· 0 citations
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