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A Regionalization Vision Navigation Method Based on Deep Reinforcement Learning
Received date: 2019-09-26
Online published: 2021-06-01
Aimed at the problems of navigation in distributed environment of a mobile robot, a regionalization vision navigation method based on deep reinforcement learning is proposed. First, considering the characteristics of distributed environment, the independent submodule learning control strategy is used in different regions and the regionalization model is built to switch and combine navigation control strategies. Then, in order to make the robot have a better goal-oriented behavior, reward prediction task is integrated into the submodule, and reward sequence is played back in combination with the experience pool. Finally, depth limitation is added to the primitive exploration strategy to prevent the traversal stagnation caused by collision. The results show that the application of reward prediction and depth obstacle avoidance is helpful to improve navigation performance. In the process of multi-area environment test, the regionalization model shows the advantages that the single model does not have in terms of training time and rewards, indicating that it can better deal with large-scale navigation. In addition, the experiment is conducted in the first-person 3D environment, and the state is partially observable, which is conducive to practical application.
LI Peng, RUAN Xiaogang, ZHU Xiaoqing, CHAI Jie, REN Dingqi, LIU Pengfei . A Regionalization Vision Navigation Method Based on Deep Reinforcement Learning[J]. Journal of Shanghai Jiaotong University, 2021 , 55(5) : 575 -585 . DOI: 10.16183/j.cnki.jsjtu.2019.277
| [1] | 徐德. 室内移动式服务机器人的感知、定位与控制[M]. 北京: 科学出版社, 2008. |
| [1] | XU De. Perception, positioning and control of indoor mobile service robot[M]. Beijing: Science Press, 2008. |
| [2] | STEFFENACH H A, WITTER M, MOSER M B, et al. Spatial memory in the rat requires the dorsola-teral band of the entorhinal cortex[J]. Neuron, 2005, 45(2):301-313. |
| [3] | ARULKUMARAN K, DEISENROTH M, BRUNDAGE M, et al. A brief survey of deep reinforcement learning, (2017-09-28)[2019-08-20]. https://arxiv.org/pdf/1708.05866.pdf |
| [4] | MIROWSKI P, PASCANU R, VIOLA F, et al. Learning to navigate in complex environments,(2017-01-13)[2019-08-13]. https://arxiv.org/pdf/1611.03673.pdf |
| [5] | ZHU Y, MOTTAGHI P, KOLVE E, et al. Target-driven visual navigation in indoor scenes using deep reinforcement learning [C]//IEEE International Conference on Robotics and Automation. Singapore: IEEE, 2017, 3357-3364. |
| [6] | JADERBERG M, MNIH V, CZARNECKI W M, et al. Reinforcement learning with unsupervised auxiliary tasks[EB/OL]. (2016-11-16)[2019-04-20]. https://arxiv.org/pdf/1611.05397.pdf |
| [7] | OH J, CHOCKALINGAM V, SATINDER P, et al. Control of memory, active perception, and action in minecraft[EB/OL]. (2016-05-30)[2019-05-07]. https://arxiv.org/pdf/1605.09128.pdf |
| [8] | 黄健, 严胜刚. 基于区域划分自适应粒子群优化的超短基线定位算法[J]. 控制与决策, 2019, 9(34):2023-2030. |
| [8] | HUANG Jian, YAN Shenggang. Ultra-short baseline positioning algorithm based on region-division adaptive particle swarm optimization[J]. Control and Decision, 2019, 9(34):2023-2030. |
| [9] | 张俊, 田慧敏. 一种基于边指针搜索及区域划分的三角剖分算法[J]. 自动化学报, 2021, 47(1):100-107. |
| [9] | ZHANG Jun, TIAN Huimin. A triangulation algorithm based on edge-pointer search and region-division[J]. Acta Automatica Sinica, 2021, 47(1):100-107. |
| [10] | RUAN X G, REN D Q, ZHU X Q, et al. Optimized data association based on Gaussian mixture model[J]. IEEE Access, 2020, 8:2590-2598. |
| [11] | 朱续涛, 何晓斌, 刘悦, 等. 一种简易的脑片图像的半自动区域划分及细胞计数方法[J]. 波谱学杂志, 2018, 2(35):133-140. |
| [11] | ZHU Xutao, HE Xiaobin, LIU Yue, et al. A Convenient semi-automatic method for analyzing brain sections: Registration, segmentation and cell counting[J]. Chinese Journal of Magnetic Resonance, 2018, 2(35):133-140. |
| [12] | KULKARNI T D, SAEEDI A, GAUTAM S, et al. Deep successor reinforcement learning, (2016-06-08)[2019-05-21]. https://arxiv.org/pdf/1606.02396.pdf |
| [13] | MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540):529-533. |
| [14] | SHIH H H, SHAO H C, PING T W, et al. Distributed deep reinforcement learning based indoor visual navigation [C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, Spain: IEEE, 2018: 2531-2537. |
| [15] | TESSLER C, GIVONY S, ZAHAVY T, et al. A deep hierarchical approach to lifelong learning in minecraft, (2016-11-30)[2019-06-05]. https://arxiv.org/pdf/1604.07255.pdf |
| [16] | HAUSKNECHT M, STONE P. Deep recurrent Q-learning for partially observable MDPs[EB/OL].(2017-01-11)[2019-05-17]. https://arxiv.org/pdf/1507.06527.pdf |
| [17] | RIEDMILLER M. Neural fitted Q iteration—First experiences with a data efficient neural reinforcement learning method [C]//16th European Conference on Machine Learning. Berlin, Germany: Springer-Verlag, 2005: 279-292. |
| [18] | LANGE S, RIEDMILLER M, VOIGTLANDER A. Autonomous reinforcement learning on raw visual input data in a real world application [C]//The 2012 International Joint Conference on Neural Networks. Brisbane, Australia: IEEE, 2012: 1-8. |
| [19] | GU S X, LILLICRAP T, SUTSKEVER I, et al. Continuous Deep Q-learning with model-based acceleration [C]//33rd International Conference on Machine Learning. USA: International Machine Learning Society, 2016: 4135-4148. |
| [20] | KAELBLING L P, LITTMAN M L, CASSANDRA A R. Planning and acting in partially observable stochastic domains[J]. Artificial Intelligence, 1998, 101(1/2):99-134. |
| [21] | HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780. |
| [22] | BEATTIE C, LEIBO J Z, TEPLYASHIN D, et al. Deepmind lab, (2016-12-13)[2018-12-30]. https://arxiv.org/pdf/1612.03801.pdf |
| [23] | GERS F A, SCHMIDHUBER J, CUMMINS F. Learning to forget: Continual prediction with LSTM[J]. Neural Computation, 2000, 12(10):2451-2471. |
| [24] | MNIH V, BADIA A P, MIRZA M, et al. Asynchronous method for deep reinforcement learning [C]//33rd International Conference on Machine Learning. USA: International Machine Learning Society, 2016: 2850-2869. |
| [25] | LEI T, MING L. Towards cognitive exploration through deep reinforcement learning for mobile robots [EB/OL]. (2016-10-06)[2019-06-12]. https://arxiv.org/pdf/1610.01733.pdf |
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