[1] Sang H Q, You Y S, Sun X J et al. The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations[J]. Ocean Engineering, 223, 108709(2021).

[2] Alyasin A, Abbas E I, Hasan S D. An efficient optimal path finding for mobile robot based on dijkstra method[C], 11-14(2019).

[3] Macek K, Petrovic I, Peric N. A reinforcement learning approach to obstacle avoidance of mobile robots[C], 462-466(2002).

[4] Cimurs R, Suh I H, Lee J H. Goal-driven autonomous exploration through deep reinforcement learning[J]. IEEE Robotics and Automation Letters, 7, 730-737(2022).

[5] Wang F Y, Chen Y, Yuan S et al. Robot path planning based on self-attention mechanism combined with DDPG[J]. Computer Engineering and Applications, 60, 158-166(2024).

[6] Chaffre T, Moras J, Chan-Hon-Tong A et al. Sim-to-real transfer with incremental environment complexity for reinforcement learning of depth-based robot navigation[C], 314-323(2020).

[7] Lin H X, Wang S, Markham A et al. Towards monocular vision based obstacle avoidance through deep reinforcement learning[EB/OL]. https://arxiv.org/abs/1706.09829

[8] Jiang J H, Yang Y X, Deng Y Q et al. BEVNav: robot autonomous navigation via spatial-temporal contrastive learning in bird’s-eye view[J]. IEEE Robotics and Automation Letters, 9, 10796-10802(2024).

[9] Yu S Q, Tian Y P. AGV path planning based on Petri net and multi-agent deep reinforcement learning[J]. Control and Decision Making, 40, 1438-1446(2025).

[10] Li X Y, Zhang J H, Guo X G et al. Reinforcement learning-based optimization algorithm for energy management and path planning of robot chassis[J]. Transactions of the Chinese Society of Agricultural Engineering, 40, 175-183(2024).

[11] de Jesus J C, Kich V A, Kolling A H et al. Depth-CUPRL: depth-imaged contrastive unsupervised prioritized representations in reinforcement learning for mapless navigation of unmanned aerial vehicles[C], 10579-10586(2022).

[12] Lin Z, Wang C, Wu S H et al. Multimodal task attention residual reinforcement learning: advancing robotic assembly in unstructured environment[J]. IEEE Robotics and Automation Letters, 10, 3900-3907(2025).

[13] Wang C, Lin Z, Liu B et al. Task attention-based multimodal fusion and curriculum residual learning for context generalization in robotic assembly[J]. Applied Intelligence, 54, 4713-4735(2024).

[14] Cheng S, Zhu H T, Qu X Q et al. A factor graph-based multiple sensors fusion method for enhanced underwater navigation of remotely operated vehicles[C], 1980-1984(2024).

[15] Yang Y S, Wang X, Ren M J. Multilayer perceptron-based fusion method for metal surface measurement data by multi-sensors incorporating photometric stereo and structured light[J]. Acta Optica Sinica, 43, 1912002(2023).

[16] Hou Y H, Zhou Y C, Liu Z Y et al. Recognizing visual position in the greenhouse using optimal transport feature aggregation[J]. Transactions of the Chinese Society of Agricultural Engineering, 40, 161-172(2024).

[17] Wang Y, Gao S, Dai Z. Laser stripe segmentation of weld seam based on CNN‑transformer hybrid networks[J]. Chinese Journal of Lasers, 51, 2402110(2024).

[18] Haarnoja T, Zhou A, Abbeel P et al. Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor[EB/OL]. https://arxiv.org/abs/1801.01290v2

[19] Choi J, Lee G, Lee C. Reinforcement learning-based dynamic obstacle avoidance and integration of path planning[J]. Intelligent Service Robotics, 14, 663-677(2021).

[20] Soutome K, Morioka K. Pedestrian avoidance navigation of mobile robots using monocular camera in urban environments based on deep reinforcement learning[C], 1-6(2024).

[21] Anasosalu Vasu P K, Gabriel J, Zhu J et al. FastViT: a fast hybrid vision transformer using structural reparameterization[C], 5762-5772(2023).

[22] Li X L, Zhou Y E, Bi T F et al. Review on key technologies of lightweight type-aware lidar[J]. Chinese Journal of Lasers, 49, 1910002(2022).

[23] Zhu K, Li B, Zhe W M et al. Collision avoidance among dense heterogeneous agents using deep reinforcement learning[J]. IEEE Robotics and Automation Letters, 8, 57-64(2023).

[24] Koenig N, Howard A. Design and use paradigms for Gazebo, an open-source multi-robot simulator[C], 2149-2154(2004).

[25] Kingmad D P, Ba J L. Adam: a method for stochastic optimization[EB/OL]. https://arxiv.org/abs/1412.6980

[26] Huang W H, Zhou Y X, He X K et al. Goal-guided transformer-enabled reinforcement learning for efficient autonomous navigation[J]. IEEE Transactions on Intelligent Transportation Systems, 25, 1832-1845(2024).

[27] Kargar E, Kyrki V. Vision transformer for learning driving policies in complex and dynamic environments[C], 1558-1564(2022).