Computer Science ›› 2023, Vol. 50 ›› Issue (1): 194-204.doi: 10.11896/jsjkx.220500241
• Artificial Intelligence • Previous Articles Next Articles
HUANG Yuzhou, WANG Lisong, QIN Xiaolin
CLC Number:
[1]CAI K,WANG C,CHENG J,et al.Mobile robot path planning in dynamic environments:a survey[J].arXiv:2006.14195,2020. [2]ZHANG H,WANG Y,YI J F,et al.Research on intelligent robot systems for emergency prevention and control of major pandemics[J].Scientia Sinica Informationis,2020,50(7):1069-1090. [3]ZHANG H,LIN W,CHEN A.Path planning for the mobile robot:A review[J].Symmetry,2018,10(10):450-466. [4]XU X,CAI P,AHMED Z,et al.Path planning and dynamic collision avoidance algorithm under COLREGs via deep reinforcement learning[J/OL].Neurocomputing,2022,468:181-197.https://doi.org/10.1016/j.neucom.2021.09.071. [5]LI W,CHEN D,LE J.Robot patrol path planning based on combined deep reinforcement learning[C]//2018 IEEE Intl. Conf. on Parallel & Distributed Processing with Applications,Ubiquitous Computing & Communications,Big Data & Cloud Computing,Social Computing & Networking,Sustainable Computing &Communications(ISPA/IUCC/BDCloud/SocialCom/SustainCom).IEEE,2018:659-666. [6]GAO J,YE W,GUO J,et al.Deep reinforcement learning for indoor mobile robot path planning[J].Sensors,2020,20(19):5493-5507. [7]PFEIFFER M,SCHAEUBLE M,NIETO J,et al.From perception to decision:A data-driven approach to end-to-end motion planning for autonomous ground robots[C]//2017 IEEE International Conference on Robotics and Automation(ICRA).IEEE,2017:1527-1533. [8]TAI L,PAOLO G,LIU M,et al.Virtual-to-real deep reinforcement learning:Continuous control of mobile robots for mapless navigation[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).IEEE,2017:31-36. [9]HAN S H,CHOI H J,BENZ P,et al.Sensor-based mobile robot navigation via deep reinforcement learning[C]//2018 IEEE International Conference on Big Data and Smart Computing(BigComp).IEEE,2018:147-154. [10]LING F,JIMENEZ-RODRIGUEZ A,PRESCOTT T J.Obstacle Avoidance Using Stereo Vision and Deep Reinforcement Lear-ning in an Animal-like Robot[C]//2019 IEEE International Conference on Robotics and Biomimetics(ROBIO).IEEE,2019:71-76. [11]XIE L,WANG S,MARKHAM A,et al.Towards monocular vision based obstacle avoidance through deep reinforcement lear-ning[J].arXiv:1706.09829,2017. [12]XIE L,WANG S,ROSA S,et al.Learning with training wheels:speeding up training with a simple controller for deep reinforcement learning[C]//2018 IEEE International Conference on Robotics and Automation(ICRA).IEEE,2018:6276-6283. [13]KULHÁNEK J,DERNER E,DE BRUIN T,et al.Vision-based navigation using deep reinforcement learning[C]//2019 Euro-pean Conference on Mobile Robots(ECMR).IEEE,2019:1-8. [14]KÄSTNER L,SHEN Z,MARX C,et al..Autonomous Navigation in Complex Environments using Memory-Aided Deep Reinforcement Learning[C]//2021 IEEE/SICE International Symposium on System Integration(SII).IEEE,2021:170-175. [15]LUONG M,PHAM C.Incremental learning for autonomousnavigation of mobile robots based on deep reinforcement lear-ning[J].Journal of Intelligent & Robotic Systems,2021,101(1):1-11. [16]FAN T,CHENG X,PAN J,et al.Crowdmove:Autonomousmapless navigation in crowded scenarios[J].arXiv:1807.07870,2018. [17]KÄSTNER L,ZHAO X,BUIYAN T,et al.Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators[C]//2021 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).IEEE,2021:1213-1220. [18]CIMURS R,SUH I H,LEE J H.Goal-driven autonomous mapping through deep reinforcement learning and planning-based navigation[J].arXiv:2103.07119,2021. [19]GULDENRING R,GÖRNER M,HENDRICH N,et al.Lear-ning local planners for human-aware navigation in indoor environments[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).IEEE,2020:6053-6060. [20]GAO P,LIU Z,WU Z,et al.A global path planning algorithm for robots using reinforcement learning[C]//2019 IEEE International Conference on Robotics and Biomimetics(ROBIO).IEEE,2019:1693-1698. [21]SICHKAR V N.Reinforcement learning algorithms in globalpath planning for mobile robot[C]//2019 International Confe-rence on Industrial Engineering,Applications and Manufacturing(ICIEAM).IEEE,2019:1-5. [22]PANOV A I,YAKOVLEV K S,SUVOROV R.Grid path planning with deep reinforcement learning:Preliminary results[J].Procedia Computer Science,2018,123:347-353. [23]MOERLAND T M,BROEKENS J,JONKER C M.Model-based reinforcement learning:A survey[J].:arXiv:2006.16712,2020. [24]LIU R,NAGEOTTE F,ZANNE P,et al.Deep reinforcementlearning for the control of robotic manipulation:a focussed mini-review[J].Robotics,2021,10(1):22-34. [25]NGUYEN H,LA H.Review of deep reinforcement learning for robot manipulation[C]//2019 Third IEEE International Confe-rence on Robotic Computing(IRC).IEEE,2019:590-595. [26]CHEN W,QIU X,CAI T,et al.Deep reinforcement learning for Internet of Things:A comprehensive survey[J].IEEE Communications Surveys & Tutorials,2021,23(3):1659-1692. [27]LI D,OKHRIN O.DDPG car-following model with real-worldhuman driving experience in CARLA[J].arXiv:2112.14602,2021. [28]DE JESUS J C,KICH V A,KOLLING A H,et al.Soft Actor-Critic for Navigation of Mobile Robots[J].Journal of Intelligent &Robotic Systems,2021,102(2):1-11. |
[1] | ZHANG Qiyang, CHEN Xiliang, ZHANG Qiao. Sparse Reward Exploration Method Based on Trajectory Perception [J]. Computer Science, 2023, 50(1): 262-269. |
[2] | WEI Nan, WEI Xianglin, FAN Jianhua, XUE Yu, HU Yongyang. Backdoor Attack Against Deep Reinforcement Learning-based Spectrum Access Model [J]. Computer Science, 2023, 50(1): 351-361. |
[3] | WANG Bing, WU Hong-liang, NIU Xin-zheng. Robot Path Planning Based on Improved Potential Field Method [J]. Computer Science, 2022, 49(7): 196-203. |
[4] | YU Bin, LI Xue-hua, PAN Chun-yu, LI Na. Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning [J]. Computer Science, 2022, 49(7): 248-253. |
[5] | LI Meng-fei, MAO Ying-chi, TU Zi-jian, WANG Xuan, XU Shu-fang. Server-reliability Task Offloading Strategy Based on Deep Deterministic Policy Gradient [J]. Computer Science, 2022, 49(7): 271-279. |
[6] | CHEN Bo-chen, TANG Wen-bing, HUANG Hong-yun, DING Zuo-hua. Pop-up Obstacles Avoidance for UAV Formation Based on Improved Artificial Potential Field [J]. Computer Science, 2022, 49(6A): 686-693. |
[7] | TAN Ren-shen, XU Long-bo, ZHOU Bing, JING Zhao-xia, HUANG Xiang-sheng. Optimization and Simulation of General Operation and Maintenance Path Planning Model for Offshore Wind Farms [J]. Computer Science, 2022, 49(6A): 795-801. |
[8] | XIE Wan-cheng, LI Bin, DAI Yue-yue. PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing [J]. Computer Science, 2022, 49(6): 3-11. |
[9] | HONG Zhi-li, LAI Jun, CAO Lei, CHEN Xi-liang, XU Zhi-xiong. Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration [J]. Computer Science, 2022, 49(6): 149-157. |
[10] | LI Peng, YI Xiu-wen, QI De-kang, DUAN Zhe-wen, LI Tian-rui. Heating Strategy Optimization Method Based on Deep Learning [J]. Computer Science, 2022, 49(4): 263-268. |
[11] | OUYANG Zhuo, ZHOU Si-yuan, LYU Yong, TAN Guo-ping, ZHANG Yue, XIANG Liang-liang. DRL-based Vehicle Control Strategy for Signal-free Intersections [J]. Computer Science, 2022, 49(3): 46-51. |
[12] | SHI Dian-xi, SU Ya-qian-wen, LI Ning, SUN Yi-xuan, ZHANG Yong-jun. Multi-UAV Cooperative Exploring for Large Unknown Indoor Environment Based on Behavior Tree [J]. Computer Science, 2022, 49(11A): 210900083-11. |
[13] | CAI Yue, WANG En-liang, SUN Zhe, SUN Zhi-xin. Study on Dual Sequence Decision-making for Trucks and Cargo Matching Based on Dual Pointer Network [J]. Computer Science, 2022, 49(11A): 210800257-9. |
[14] | DAI Shan-shan, LIU Quan. Action Constrained Deep Reinforcement Learning Based Safe Automatic Driving Method [J]. Computer Science, 2021, 48(9): 235-243. |
[15] | CHENG Zhao-wei, SHEN Hang, WANG Yue, WANG Min, BAI Guang-wei. Deep Reinforcement Learning Based UAV Assisted SVC Video Multicast [J]. Computer Science, 2021, 48(9): 271-277. |
|