Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (10): 16-29.DOI: 10.3778/j.issn.1002-8331.2309-0203
• Research Hotspots and Reviews • Previous Articles Next Articles
YU Junqi, CHEN Yisheng, FENG Chunyong, SU Yucong, GUO Jugang
Online:
2024-05-15
Published:
2024-05-15
于军琪,陈易圣,冯春勇,苏煜聪,郭聚刚
YU Junqi, CHEN Yisheng, FENG Chunyong, SU Yucong, GUO Jugang. Review of Research on Local Path Planning for Intelligent Construction Robots[J]. Computer Engineering and Applications, 2024, 60(10): 16-29.
于军琪, 陈易圣, 冯春勇, 苏煜聪, 郭聚刚. 智能建造机器人局部路径规划研究综述[J]. 计算机工程与应用, 2024, 60(10): 16-29.
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[1] 张皓涵. 建筑施工机器人技术的应用与发展[J]. 广东建材, 2021, 37(8): 74-78. ZHANG H H. Application and development of robotics in building construction[J]. Guangdong Building Materials, 2021, 37(8): 74-78. [2] LIU W, LI Z, SUN S, et al. Design a novel target to improve positioning accuracy of autonomous vehicular navigation system in GPS denied environments[J]. IEEE Transactions on Industrial Informatics, 2021, 17(11): 7575-7588. [3] GAO Y, CHIEN S. Review on space robotics: toward top-level science through space exploration[J]. Science Robotics, 2017, 2(7): 5074. [4] RANGAPUR I, PRASAD B K S, SURESH R. Design and development of spherical spy robot for surveillance operation[J]. Procedia Computer Science, 2020, 171: 1212-1220. [5] PATIL D, ANSARI M, TENDULKAR D, et al. A survey on autonomous military service robot[C]//Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering, 2020: 1-7. [6] HUANG X, CAO Q, ZHU X. Mixed path planning for multi-robots in structured hospital environment[J]. The Journal of Engineering, 2019(14): 512-516. [7] KAYACAN E, KAYACAN E, RAMON H, et al. Towards agrobots: trajectory control of an autonomous tractor using type-2 fuzzy logic controllers[J]. IEEE/ASME Transactions on Mechatronics, 2014, 20(1): 287-298. [8] 陈翀, 李星, 邱志强, 等. 建筑施工机器人研究进展[J]. 建筑科学与工程学报, 2022, 39(4): 58-70. CHEN C, LI X, QIU Z Q, et al. Research progress of construction robot[J]. Journal of Architecture and Civil Engineering, 2022, 39(4): 58-70. [9] HEWAWASAM H, IBRAHIM Y, KAHANDAWA G. A novel optimistic local path planner: agoraphilic navigation algorithm in dynamic environment[J]. Machines, 2022, 10(11): 1085. [10] 翟敬梅, 刘坤, 徐晓. 室内移动机器人自主导航系统设计与方法[J]. 计算机集成制造系统, 2020, 26(4): 890-899. ZHAI J M, LIU K, XU X. Autonomous indoor navigation system of mobile robot[J]. Computer Integrated Manufacturing Systems, 2020, 26(4): 890-899. [11] 李二超, 王玉华. 改进人工势场法的移动机器人避障轨迹研究[J]. 计算机工程与应用, 2022, 58(6): 296-304. LI E C, WANG Y H. Research on obstacle avoidance trajectory of mobile robot based on improved artificial potential field[J]. Computer Engineering and Applications, 2022, 58(6): 296-304. [12] 李晓旭, 马兴录, 王先鹏. 移动机器人路径规划算法综述[J]. 计算机测量与控制, 2022, 30(7): 9-19. LI X X, MA X L, WANG X P. A survey of path planning algorithms for mobile robot[J]. Computer Measurement & Control, 2022, 30(7): 9-19. [13] DAI X, LI C K, RAD A B. An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control[J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(3): 285-293. [14] KAMIL F, HONG T S, KHAKSAR W, et al. New robot navigation algorithm for arbitrary unknown dynamic environments based on future prediction and priority behavior[J]. Expert Systems with Applications, 2017, 86: 274-291. [15] ZHANG X, ZHU T, DU L, et al. Local path planning of autonomous vehicle based on an improved heuristic Bi-RRT algorithm in dynamic obstacle avoidance environment[J]. Sensors, 2022, 22(20): 7968. [16] R?SMANN C, FEITEN W, W?SCH T, et al. Trajectory modification considering dynamic constraints of autonomous robots[C]//Proceedings of the 7th German Conference on Robotics, 2012: 1-6. [17] 代婉玉, 张丽娟, 吴佳峰, 等. 改进TEB算法的局部路径规划算法研究[J]. 计算机工程与应用, 2022, 58(8): 283-288. DAI W Y, ZHANG L J, WU J F, et al. Research on local path planning algorithm based on improved TEB algorithm[J]. Computer Engineering and Applications, 2022, 58(8): 283-288. [18] 程志, 张志安, 李金芝, 等. 改进人工势场法的移动机器人路径规划[J]. 计算机工程与应用, 2019, 55(23): 29-34. CHENG Z, ZHANG Z A, LI J Z, et al. Mobile robots path planning based on improved artificial potential field[J]. Computer Engineering and Applications, 2019, 55(23): 29-34. [19] 许万, 程兆, 朱力, 等. 一种基于改进人工势场法的局部路径规划算法[J]. 电子测量技术, 2022, 45(19): 83-88. XU W, CHENG Z, ZHU L, et al. A local path planning algorithm based on improved artificial potential field method[J]. Electronic Measurement Technology, 2022, 45(19): 83-88. [20] 易先军, 耿翰夫, 付龙, 等. 模糊改进人工势场法移动机器人路径规划[J]. 组合机床与自动化加工技术, 2021(5): 65-68. YI X J, GENG H F, FU L, et al. Mobile robot path planning based on fuzzy improved artificial potential field method[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021(5): 65-68. [21] 徐小强, 王明勇, 冒燕. 基于改进人工势场法的移动机器人路径规划[J]. 计算机应用, 2020, 40(12): 3508-3512. XU X Q, WANG M Y, MAO Y. Path planning of mobile robot based on improved artificial potential field method[J]. Journal of Computer Applications, 2020, 40(12): 3508-3512. [22] 王兵, 吴洪亮, 牛新征. 基于改进势场法的机器人路径规划[J]. 计算机科学, 2022, 49(7): 196-203. WANG B, WU H L, NIU X Z. Robot path planning based on improved potential field method[J]. Computer Science, 2022, 49(7): 196-203. [23] 杜婉茹, 王潇茵, 田涛, 等. 面向未知环境及动态障碍的人工势场路径规划算法[J]. 计算机科学, 2021, 48(2): 250-256. DU W R, WANG X Y, TIAN T, et al. Artificial potential field path planning algorithm for unknown environment and dynamic obstacles[J]. Computer Science, 2021, 48(2): 250-256. [24] 张铮, 薛波, 柯子鹏, 等. 改进人工势场算法的路径规划[J/OL].西安理工大学学报 (2023-09-04)[2023-11-29]. http://kns.cnki.net/kcms/detail/61.1294.N.20230904.1221.006.html. ZHANG Z, XUE B, KE Z P, et al. Robot local path planning based on improved artificial potential field method[J/OL]. Journal of Xi’an University of Technology (2023-09-04) [2023-11-29]. http://kns.cnki.net/kcms/detail/61.1294. N.20230904.1221.006.html. [25] 郭明皓, 姬鹏, 黄海威. 基于改进人工势场法的无人车路径规划与跟踪控制[J/OL].系统仿真学报 (2023-09-15)[2023-11-27]. https://doi.org/10.16182/j.issn1004731x.joss. 23-0768. GUO M H, JI P, HUANG H W. Unmanned vehicle path planning and tracking control based on improved artificial potential field method[J/OL]. Journal of System Simulation (2023-09-15) [2023-11-27]. https://doi.org/10.16182/j.issn1004731x. joss.23-0768. [26] 郑凯林, 韩宝玲, 王新达. 基于改进TEB算法的阿克曼机器人运动规划系统[J]. 科学技术与工程, 2020, 20(10): 3997-4003. ZHENG K L, HAN B L, WANG X D. Ackerman robot motion planning system based on improved TEB algorithm[J]. Science Technology and Engineering, 2020, 20(10): 3997-4003. [27] 陈曦, 石博强, 郭辉. 优化TEB算法的四轮差速机器人局部路径规划[J]. 软件, 2022, 43(6): 133-137. CHEN X, SHI B Q, GUO H. Local path planning of four-wheel differential robot based on optimized TEB algorithm[J]. Software, 2022, 43(6): 133-137. [28] 文郁, 黄江帅, 江涛, 等. 安全平滑的改进时间弹性带轨迹规划算法[J]. 控制与决策, 2022, 37(8): 2008-2016. WEN Y, HUANG J S, JIANG T, et al. Safe and smooth improved time elastic band trajectory planning algorithm[J]. Control and Decision, 2022, 37(8): 2008-2016. [29] 陈奕梅, 沈建峰, 李柄棋. 改进TEB算法的多机器人动态避障策略研究[J]. 电光与控制, 2022, 29(5): 107-112. CHEN Y M, SHEN J F, LI B Q. On dynamic obstacle avoidance strategy for multi-robot with improved TEB algorithm[J]. Electronics Optics & Control, 2022, 29(5): 107-112. [30] 陈纪廷, 郭晨, 刘毅. 基于时间弹性带的移动机器人路径优化方法[J]. 科学技术与工程, 2021, 21(26): 11212-11219. CHEN J T, GUO C, LIU Y. Path optimization method for mobile robot based on timed elastic band[J]. Science Technology and Engineering, 2021, 21(26): 11212-11219. [31] 吴涛, 谢志军, 陈科伟. 改进D*lite和时间弹性带法的移动机器人路径规划[J]. 传感技术学报, 2022, 35(4): 486-494. WU T, XIE Z J, CHEN K W. Path planning base on improved D*lite and time elastic band for mobile robot[J]. Chinese Journal of Sensors and Actuators, 2022, 35(4): 486-494. [32] 高欣宇, 田国富. 融合改进A*和TEB算法的机器人路径规划[J]. 组合机床与自动化加工技术, 2023(8): 42-46. GAO X Y, TIAN G F. Robot path planning based on the fusion of improved A* and TEB algorithm[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2023(8): 42-46. [33] 徐定明, 李子信, 张怡俊. 建筑机器人融合改进的A*与TEB算法的运动规划研究[J]. 智能计算机与应用, 2022, 12(12): 55-61. XU D M, LI Z X, ZHANG Y J. Research on motion planning of construction robots based on improved A* and TEB algorithm[J]. Intelligent Computer and Applications, 2022, 12(12): 55-61. [34] XU Q L, YU T, BAI J. The mobile robot path planning with motion constraints based on bug algorithm[C]//Proceedings of the 2017 Chinese Automation Congress, 2017: 2348-2352. [35] MELO W D, JORGE D, MARQUES V. Low-cost thermal explorer robot using a hybrid neural networks and intelligent bug algorithm model[J]. International Journal of Computer Applications in Technology, 2021, 65(3): 245-252. [36] SIVARANJANI S, NANDESH D A, GAYATHRI K, et al. An investigation of bug algorithms for mobile robot navigation and obstacle avoidance in two-dimensional unknown static environments[C]//Proceedings of the 2021 International Conference on Communication Information and Computing Technology, 2021: 1-6. [37] 孙伶玉, 付主木, 陶发展, 等. 改进人工势场的智能车避障算法研究[J]. 河南科技大学学报 (自然科学版), 2022, 43(5): 28-34. SUN L Y, FU Z M, TAO F Z, et al. Obstacle avoidance algorithm of autonomous vehicle based on an improved artificial potential field[J]. Journal of Henan University of Science and Technology (Natural Science), 2022, 43(5): 28-34. [38] NELOY M, DAS M, BARUA P, et al. An intelligent obstacle and edge recognition system using bug algorithm[J]. American Scientific Research Journal for Engineering, Technology, and Sciences, 2020, 64(1): 133-143. [39] SOUIDI M E H, PIAO S, LI G. Mobile agents path planning based on an extension of bug-algorithms and applied to the pursuit-evasion game[J]. Web Intelligence, 2017, 15(4): 325-334. [40] DAS S K, ROY K, PANDEY T, et al. Modified critical point—a bug algorithm for path planning and obstacle avoiding of mobile robot[C]//Proceedings of the 2020 International Conference on Communication and Signal Processing, 2020: 351-356. [41] 卞永明, 季鹏成, 周怡和, 等. 基于改进型DWA的移动机器人避障路径规划[J]. 中国工程机械学报, 2021, 19(1): 44-49. BIAN Y M, JI P C, ZHOU Y H, et al. Obstacle avoidance path planning of mobile robot based on improved DWA[J]. Chinese Journal of Construction Machinery, 2021, 19(1): 44-49. [42] 张瑜, 宋荆洲, 张琪祁. 基于改进动态窗口法的户外清扫机器人局部路径规划[J]. 机器人, 2020, 42(5): 617-625. ZHANG Y, SONG J Z, ZHANG Q Q. Local path planning of outdoor cleaning robot based on an improved DWA[J]. Robot, 2020, 42(5): 617-625. [43] 智宝岩. 矿区井下无人运载车辆全局约束下的局部路径规划方法[J]. 中国设备工程, 2022(24): 141-142. ZHI Y B. Local path planning method for unmanned underground delivery vehicles in mining areas under global constraints[J]. China Plant Engineering, 2022(24): 141-142. [44] 王瑞民, 张江, 崔俊杰, 等. 基于改进DWA算法的无人车避障研究[J]. 机械设计与制造工程, 2023, 52(5): 37-42. WANG R M, ZHANG J, CUI J J, et al. Research on obstacle avoidance of unmanned vehicle based on improved DWA algorithm[J]. Machine Design and Manufacturing Engineering, 2023, 52(5): 37-42. [45] 王豪杰, 马向华, 代婉玉, 等. 改进DWA算法的移动机器人避障研究[J]. 计算机工程与应用, 2023, 59(6): 326-332. WANG H J, MA X H, DAI W Y, et al. Research on obstacle avoidance of mobile robot based on improved DWA algorithm[J]. Computer Engineering and Applications, 2023, 59(6): 326-332. [46] 王旭扬, 梁志伟, 高翔, 等. 基于改进DWA算法的足球机器人局部轨迹规划[J]. 国外电子测量技术, 2023, 42(8): 1-9. WANG X Y, LIANG Z W, GAO X, et al. Local trajectory planning of soccer robot based on improved DWA algorithm[J]. Foreign Electronic Measurement Technology, 2023, 42(8): 1-9. [47] 王凡, 李铁军, 刘今越, 等. 基于BIM的建筑机器人自主路径规划及避障研究[J]. 计算机工程与应用, 2020, 56(17): 224-230. WANG F, LI T J, LIU J Y, et al. Research on autonomous path planning and obstacle avoidance of building robot based on BIM[J]. Computer Engineering and Applications, 2020, 56(17): 224-230. [48] ALDAO E, GONZáLEZ-DESANTOS L M, MICHINEL H, et al. UAV obstacle avoidance algorithm to navigate in dynamic building environments[J]. Drones, 2022, 6(1): 16. [49] 刘子毅, 李铁军, 孙晨昭, 等. 基于BIM的建筑机器人自主导航策略优化研究[J]. 计算机工程与应用, 2022, 58(15): 302-308. LIU Z Y, LI T J, SUN C Z, et al. Research on optimization of autonomous navigation strategy of construction robot based on BIM[J]. Computer Engineering and Applications, 2022, 58(15): 302-308. [50] 谷万, 徐振, 郭帅. 基于A*和改进动态窗口法的建筑移动机器人路径规划[J]. 工业控制计算机, 2022, 35(8): 87-90. GU F, XU Z, GUO S. Path planning of construction mobile robot based on A* and improved dynamic window approach[J]. Industrial Control Computer, 2022, 35(8): 87-90. [51] 闫皎洁, 张锲石, 胡希平. 基于强化学习的路径规划技术综述[J]. 计算机工程, 2021, 47(10): 16-25. YAN J J, ZHANG Q S, HU X P. Review of path planning techniques based on reinforcement learning[J]. Computer Engineering, 2021, 47(10): 16-25. [52] 朱洁. 基于深度强化学习的移动机器人路径规划研究[D]. 临沂: 临沂大学, 2023. ZHU J. Research on path planning for mobile robots based on deep reinforcement learning[D]. Linyi: Linyi University,2023. [53] 陈坚. 基于机器学习的网联无人机通信优化研究[D]. 长春: 吉林大学, 2022. CHEN J. Research on communication optimization of cellular-connected UAV based on machine learning[D]. Changchun: Jilin University, 2022. [54] 王科银, 石振, 杨正才, 等. 改进强化学习算法应用于移动机器人路径规划[J]. 计算机工程与应用, 2021, 57(18): 270-274. WANG K Y, SHI Z, YANG Z C, et al. Path planning for mobile robot using improved reinforcement learning algorithm[J]. Computer Engineering and Applications, 2021, 57(18): 270-274. [55] 陈起源. 基于强化学习的变电站巡检机器人路径规划算法[D]. 西安: 西安建筑科技大学, 2022. CHEN Q Y. Path planning algorithm for substation inspection robot based on reinforcement learning[D]. Xi’an: Xi’an University of Architecture and Technology, 2022. [56] 罗洁, 王中训, 潘康路, 等. 基于改进人工势场法的无人车路径规划算法[J]. 电子设计工程, 2022, 30(17): 90-94. LUO J, WANG Z X, PAN K L, et al. Unmanned vehicle path planning algorithm based on improved artificial potential field method[J]. Electronic Design Engineering, 2022, 30(17): 90-94. [57] ALI N, KAMARUDIN K, BAKAR M A A, et al. 2D LiDAR based reinforcement learning for multi-target path planning in unknown environment[J]. IEEE Access, 2023, 11: 35541-35555. [58] 问泽藤. 基于深度强化学习的移动机器人自主路径规划算法研究[D]. 秦皇岛: 燕山大学, 2022. WEN Z T. Research on autonomous path planning algorithm of mobile robots based on deep reinforcement learning[D]. Qinhuangdao: Yanshan University, 2022. [59] 杨思明, 单征, 曹江, 等. 基于模型的强化学习在无人机路径规划中的应用[J]. 计算机工程, 2022, 48(12): 255-260. YANG S M, SHAN Z, CAO J, et al. Application of model-based reinforcement learning in path planning of unmanned aerial vehicle[J]. Computer Engineering, 2022, 48(12): 255-260. [60] 李永迪, 李彩虹, 张耀玉, 等. 基于改进SAC算法的移动机器人路径规划[J]. 计算机应用, 2023, 43(2): 654-660. LI Y D, LI C H, ZHANG Y Y, et al. Mobile robot path planning based on improved SAC algorithm[J]. Journal of Computer Applications, 2023, 43(2): 654-660. [61] WU R, GU F, LIU H, et al. UAV path planning based on multicritic-delayed deep deterministic policy gradient[J]. Wireless Communications and Mobile Computing, 2022. DOI: 10.1155/2022/9017079. [62] 余久方, 尧海昌.基于改进融合深度强化学习的机器人路径规划[J]. 组合机床与自动化加工技术, 2023(5): 19-22. YU J F, YAO H C. Robot path planning based on improved fusion deep reinforcement learning[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2023(5): 19-22. [63] GU Y, ZHU Z, LV J, et al. DM-DQN: dueling munchausen deep Q network for robot path planning[J]. Complex & Intelligent Systems, 2023, 9(4): 4287-4300. [64] LI J, WANG X, TANG S, et al. Unsupervised reinforcement learning of transferable meta-skills for embodied navigation[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 12123-12132. [65] ZHANG W, HE C, WANG T. Multi-task decomposition architecture based deep reinforcement learning for obstacle avoidance[C]//Proceedings of the 2020 Chinese Automation Congress, 2020: 2735-2740. [66] TAI L, LIU M. Towards cognitive exploration through deep reinforcement learning for mobile robots[J]. arXiv:1610. 01733, 2016. [67] 朱少凯, 孟庆浩, 金晟, 等. 基于深度强化学习的室内视觉局部路径规划[J]. 智能系统学报, 2022, 17(5): 908-918. ZHU S K, MENG Q H, JIN S, et al. Indoor visual local path planning based on deep reinforcement learning[J]. CAAI Transactions on Intelligent Systems, 2022, 17(5): 908-918. [68] 任伟建, 王飞, 吕微. 分层模糊控制的移动机器人路径规划[J]. 科学技术与工程, 2010, 10(10): 2317-2321. REN W J, WANG F, LV W. The path planning of mobile robots based on hierarchical fuzzy systems[J]. Science Technology and Engineering, 2010, 10(10): 2317-2321. [69] 张俊溪, 米国际, 王鑫, 等. 基于进化算法和模糊控制的机器人路径规划[J]. 计算机技术与发展, 2018, 28(6): 49-52. ZHANG J X, MI G J, WANG X, et al. Research on path planning of robot based on evolutionary algorithm and fuzzy control algorithm[J]. Computer Technology and Development, 2018, 28(6): 49-52. [70] 郭娜, 李彩虹, 王迪, 等. 基于模糊控制的移动机器人局部路径规划[J]. 山东理工大学学报(自然科学版), 2020, 34(4): 24-29. GUO N, LI C H, WANG D, et al. Local path planning of mobile robot based on fuzzy control[J]. Journal of Shandong University of Technology (Natural Science Edition), 2020, 34(4): 24-29. [71] FU H, LIU X. A path planning method for mobile robots based on fuzzy firefly algorithms[J]. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 2021, 14(9): 3040-3045. [72] 石振新, 冯剑波, 王衍学. 基于MPC和模糊控制的智能汽车路径追踪研究[J]. 车辆与动力技术, 2022(2): 7-11. SHI Z X, FENG J B, WANG Y X. Research on intelligent vehicle path tracking based on MPC and fuzzy control[J]. Vehicle & Power Technology, 2022(2): 7-11. [73] 刘翰培, 王东署, 汪宇轩, 等. 移动机器人路径规划的模糊人工势场法研究[J]. 控制工程, 2022, 29(1): 33-38. LIU H P, WANG D S, WANG Y X, et al. Research of path planning for mobile robots based on fuzzy artificial potential field method[J]. Control Engineering of China, 2022, 29(1): 33-38. [74] 张九龙, 王晓峰, 芦磊, 等. 若干新型智能优化算法对比分析研究[J]. 计算机科学与探索, 2022, 16(1): 88-105. ZHANG J L, WANG X F, LU L, et al. Analysis and research of several new intelligent optimization algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(1): 88-105. [75] 何世琼, 陈雨.一种改进的人工势场法路径规划算法[J]. 现代计算机, 2022, 28(15): 17-23. HE S Q, CHEN Y. An improved path planning algorithm for artificial potential field method[J]. Modern Computer, 2022, 28(15): 17-23. [76] 李丽娜, 郭永强, 张晓东, 等. 萤火虫算法结合人工势场法的机器人路径规划[J]. 计算机工程与应用, 2018, 54(20): 104-109. LI L N, GUO Y Q, ZHANG X D, et al. Path planning algorithm for robot based on firefly algorithm combined with artificial potential field method[J]. Computer Engineering and Applications, 2018, 54(20): 104-109. [77] 丁承君, 阎欣怡, 冯玉伯, 等. 基于APF的AGV局部路径规划改进算法研究[J]. 计算机工程与应用, 2022, 58(22): 305-312. DING C J, YAN X Y, FENG Y B, et al. Improved algorithm of AGV local path planning based on APF[J]. Computer Engineering and Applications, 2022, 58(22): 305-312. [78] 万晓凤, 胡伟, 郑博嘉, 等. 基于改进蚁群算法与Morphin算法的机器人路径规划方法[J]. 科技导报, 2015, 33(3): 84-89. WAN X F, HU W, ZHENG B J, et al. Robot path planning method based on improved ant colony algorithm and Morphin algorithm[J]. Science & Technology Review, 2015, 33(3): 84-89. [79] 陈丹凤, 雷昊, 刘俊朗, 等. 基于强化蚁群算法的机器人路径规划研究[J]. 兵器装备工程学报, 2023, 44(6): 239-245. CHEN D F, LEI H, LIU J L, et al. Research on robot path planning according to reinforcement learning based ant colony algorithm[J]. Journal of Ordnance Equipment Engineering, 2023, 44(6): 239-245. [80] BAKDI A, HENTOUT A, BOUTAMI H, et al. Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control[J]. Robotics and Autonomous Systems, 2017, 89: 95-109. [81] 王鹏凯, 梁中华, 杨阔, 等. 基于深度强化学习和动态窗口法的移动机器人路径规划[J]. 计算机与数字工程, 2021, 49(10): 2017-2022. WANG P K, LIANG Z H, YANG K, et al. Path planning for mobile robot based on deep reinforcement learning and dynamic window approach[J]. Computer & Digital Engineering, 2021, 49(10): 2017-2022. [82] XING X R, GUO J, LIANG Z G, et al. DDPG-based improved seeker optimization algorithm for robot path planning[C]//Proceedings of the 2022 2nd International Conference on Computer, Control and Robotics, 2022: 27-31. [83] LEE W C, LIM M C, CHOI H L. Extendable navigation network based reinforcement learning for indoor robot exploration[C]//Proceedings of the 2021 IEEE International Conference on Robotics and Automation, 2021: 11508-11514. [84] KIM P, PARK J, CHO Y K, et al. UAV-assisted autonomous mobile robot navigation for as-is 3D data collection and registration in cluttered environments[J]. Automation in Construction, 2019, 106: 102918. [85] CHANG S, SIU M F F, LI H. Development of a fuzzy logic controller for autonomous navigation of building inspection robots in unknown environments[J]. Journal of Computing in Civil Engineering, 2023, 37(4): 04023014. [86] XUE J, KONG X, WANG G, et al. Path planning algorithm in complex environment based on DDPG and MPC[J]. Journal of Intelligent & Fuzzy Systems, 2023, 45(1): 1817-1831. [87] CHOI J, LEE G, LEE C. Reinforcement learning-based dynamic obstacle avoidance and integration of path planning[J]. Intelligent Service Robotics, 2021, 14: 663-677. [88] LEE M F R, YUSUF S H. Mobile robot navigation using deep reinforcement learning[J]. Processes, 2022, 10(12): 2748. [89] 程浩鹏, 朱涵, 杨高奇, 等. 深度强化学习及智能路径规划应用综述[J]. 现代计算机, 2022, 28(21): 1-10. CHENG H P, ZHU H, YANG G Q, et al. Review of deep reinforcement learning and its application in intelligent path planning algorithms[J]. Modern Computer, 2022, 28(21): 1-10. [90] 毛建旭, 贺振宇, 王耀南, 等. 电力巡检机器人路径规划技术及应用综述[J]. 控制与决策, 2023, 38(11): 3009-3024. MAO J X, HE Z Y, WANG Y N, et al. A review of research and applications on path planning technology for power inspection robots[J]. Control and Decision, 2023, 38(11): 3009-3024. |
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