·1228 工程科学学报,第42卷.第9期 明,不论是在路径转移距离方面还是在路径转移 [13]Bircher A,Kamel M,Alexis K,et al.Receding horizon path 时间方面,BNN算法均具有高效性,未来的工作 planning for 3D exploration and surface inspection.Autonom 任务是采用一种主动搜索算法完成下一子区域最 Robots,.2018,42(2):291 佳起始点的搜索,要求该搜索算法获得的最佳起 [14]Wang H J,Yu Y,Yuan Q B.Application of Dijkstra algorithm in robot path-planning Second International Conference on 始点与上一结点之间具有较短路径消耗 Mechanic Automation Control Engineering.Hohhot,2011:1067 [15]Fu B,Chen L,Zhou Y T,et al.An improved A*algorithm for the 参考文献 industrial robot path planning with high success rate and short [1]Sang L H,Fu M C,Feng Y H.Progress and prospect of research length.Rob Autonomous Syst,2018,106:26 on land reclamation planning and design in mining area.Coal Sci [16]Wei K,Ren B Y.A method on dynamic path planning for robotic Technol,2018,46(2):243 manipulator autonomous obstacle avoidance based on an improved (桑李红,付梅臣,冯洋欢.煤矿区土地复垦规划设计研究进展 RRT algorithm.Sensors,2018,18(2):571 及展望.煤炭科学技术,2018.46(2):243) [17]Rashid R,Perumal N,Elamvazuthi I,et al.Mobile robot path [2] Xu B,Xu M,Chen L P,et al.Review on coverage path planning planning using Ant Colony Optimization//2016 2nd IEEE algorithm for intelligent machinery.Comput Meas Control,2016, International Symposium on Robotics and Manufacturing 24(10):1 Automation (ROMA).Ipoh,2016:1 (徐博,徐旻,陈立平,等.智能机械全覆盖路径规划算法综述 [18]Zhang C,Li Q,Chen P,et al.Improved ant colony optimization 计算机测量与控制,2016,24(10):1) based on particle swarm optimization and its application.J Univ [3] Hsu P M,Lin C L,Yang M Y.On the complete coverage path Sci Technol Beijing,2013,35(7):955 planning for mobile robots.JIntell Robot Syst,2014,74(3-4):945 (张超,李擎,陈鹏,等.一种基于粒子群参数优化的改进蚊群算 [4]Mac TT,Copot C,Tran DT,et al.Heuristic approaches in robot 法及其应用.北京科技大学学报,2013,35(7):955) path planning:a survey.Rob Auonom Syst,2016,86:13 [19]Tian Z J,Gao X H,Zhang M X.Path planning based on the [5] Wang Z L,Li H,Zhang X L.Construction waste recycling robot improved artificial potential field of coal mine dynamic target for nails and screws:computer vision technology and neural navigation.J China Coal Soc,2016,41(Suppl 2):589 network approach.Autom Construct,2019,97:220 (田子建,高学浩,张梦霞.基于改进人工势场的矿井导航装置 [6]Wang Y N,Pan Q,Chen Y J.Path planning method based on 路径规划.煤炭学报,2016,41(增刊2):589) improved biologically inspired neural network.Control Eng [20]Chen E K,Wu M H,Zhang Y J.Path planning for coal mine Chia,2018,25(4):541 rescue robot in complex environment.Coal Technol,2018, (王耀南,潘琪,陈彦杰.改进型生物激励神经网络的路径规划 37(10):301 方法.控制工程,2018,25(4):541) (陈尔奎,吴梅花,张英杰.复杂环境下煤矿救灾机器人路径规 [7]Zhu D Q,Cao X.Sun B.et al.Biologically inspired self- 划.煤炭技术,2018,37(10):301) organizing map applied to task assignment and path planning of an [21]Sun J,Chen Z H,Wang P,et al.Multi-region coverage method AUV system.IEEE Trans Cognitive Dey Syst,2018,10(2):304 based on cost map and minimal tree for mobile robot.Robot,2015, [8]Zhu D Q,Yan M Z.Survey on technology of mobile robot path 37(4):435 planning.Control Des,2010,25(7):961 (孙建,陈宗海,王鹏,等.基于代价地图和最小树的移动机器人 (朱大奇,颜明重.移动机器人路径规划技术综述.控制与决策 多区域覆盖方法.机器人,2015,37(4):435) 2010,25(7):961) [22]Hameed I A,Bochtis D,Sorensen C A.An optimized field [9]Liu G,Li X,Kang X,et al.Automatic navigation path planning coverage planning approach for navigation of agricultural robots in method for land leveling based on GNSS.Trans Chin Soc Agric fields involving obstacle areas.IntJAdy Rob Syst,2013,10(5):1 Mach,2016,47(增f刊1):21 [23]Tang Q S.Application and research on tree structure based on (刘刚,李笑,康熙,等.基于GNSS的农田平整自动导航路径规 depth-first algorithm.Comput Technol Dev,2014,24(9):226 划方法.农业机械学报,2016,47(增刊1):21) (唐青松.深度优先算法在创建树形结构中的应用研究.计算机 [10]Sucan I A,Moll M,Kaveraki L E.The open motion planning 技术与发展,2014,24(9):226) library.IEEE Rob Autom Mag,2012.19(4):72 [24]Luo C M,Yang S X,Li X D,et al.Neural-dynamics-driven [11]Oksanen T,Visala A.Coverage path planning algorithms for complete area coverage navigation through cooperation of multiple agricultural field machines.J Field Rob,2009,26(8):651 mobile robots.IEEE Trans Ind Electron,2017,64(1):750 [12]Palleja T,Tresanchez M,Teixido M,et al.Modeling floor- [25]Yang S X,Meng M.An efficient neural network approach to cleaning coverage performances of some domestic mobile robots dynamic robot motion planning.Neural Nenworks,2000,13(2): in a reduced scenario.Rob Autonom Syst,2010,58(1):37 143明,不论是在路径转移距离方面还是在路径转移 时间方面,BINN 算法均具有高效性. 未来的工作 任务是采用一种主动搜索算法完成下一子区域最 佳起始点的搜索,要求该搜索算法获得的最佳起 始点与上一结点之间具有较短路径消耗. 参 考 文 献 Sang L H, Fu M C, Feng Y H. Progress and prospect of research on land reclamation planning and design in mining area. Coal Sci Technol, 2018, 46(2): 243 (桑李红, 付梅臣, 冯洋欢. 煤矿区土地复垦规划设计研究进展 及展望. 煤炭科学技术, 2018, 46(2):243) [1] Xu B, Xu M, Chen L P, et al. Review on coverage path planning algorithm for intelligent machinery. Comput Meas Control, 2016, 24(10): 1 (徐博, 徐旻, 陈立平, 等. 智能机械全覆盖路径规划算法综述. 计算机测量与控制, 2016, 24(10):1) [2] Hsu P M, Lin C L, Yang M Y. On the complete coverage path planning for mobile robots. J Intell Robot Syst, 2014, 74(3-4): 945 [3] Mac T T, Copot C, Tran D T, et al. Heuristic approaches in robot path planning: a survey. Rob Autonom Syst, 2016, 86: 13 [4] Wang Z L, Li H, Zhang X L. Construction waste recycling robot for nails and screws: computer vision technology and neural network approach. Autom Construct, 2019, 97: 220 [5] Wang Y N, Pan Q, Chen Y J. Path planning method based on improved biologically inspired neural network. Control Eng China, 2018, 25(4): 541 (王耀南, 潘琪, 陈彦杰. 改进型生物激励神经网络的路径规划 方法. 控制工程, 2018, 25(4):541) [6] Zhu D Q, Cao X, Sun B, et al. Biologically inspired selforganizing map applied to task assignment and path planning of an AUV system. IEEE Trans Cognitive Dev Syst, 2018, 10(2): 304 [7] Zhu D Q, Yan M Z. Survey on technology of mobile robot path planning. Control Des, 2010, 25(7): 961 (朱大奇, 颜明重. 移动机器人路径规划技术综述. 控制与决策, 2010, 25(7):961) [8] Liu G, Li X, Kang X, et al. Automatic navigation path planning method for land leveling based on GNSS. Trans Chin Soc Agric Mach, 2016, 47(增刊1): 21 (刘刚, 李笑, 康熙, 等. 基于GNSS的农田平整自动导航路径规 划方法. 农业机械学报, 2016, 47(增刊1):21) [9] Sucan I A, Moll M, Kaveraki L E. The open motion planning library. IEEE Rob Autom Mag, 2012, 19(4): 72 [10] Oksanen T, Visala A. Coverage path planning algorithms for agricultural field machines. J Field Rob, 2009, 26(8): 651 [11] Palleja T, Tresanchez M, Teixido M, et al. Modeling floorcleaning coverage performances of some domestic mobile robots in a reduced scenario. Rob Autonom Syst, 2010, 58(1): 37 [12] Bircher A, Kamel M, Alexis K, et al. Receding horizon path planning for 3D exploration and surface inspection. Autonom Robots, 2018, 42(2): 291 [13] Wang H J, Yu Y, Yuan Q B. Application of Dijkstra algorithm in robot path-planning // Second International Conference on Mechanic Automation & Control Engineering. Hohhot, 2011: 1067 [14] Fu B, Chen L, Zhou Y T, et al. An improved A* algorithm for the industrial robot path planning with high success rate and short length. Rob Autonomous Syst, 2018, 106: 26 [15] Wei K, Ren B Y. A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm. Sensors, 2018, 18(2): 571 [16] Rashid R, Perumal N, Elamvazuthi I, et al. Mobile robot path planning using Ant Colony Optimization// 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA). Ipoh, 2016: 1 [17] Zhang C, Li Q, Chen P, et al. Improved ant colony optimization based on particle swarm optimization and its application. J Univ Sci Technol Beijing, 2013, 35(7): 955 (张超, 李擎, 陈鹏, 等. 一种基于粒子群参数优化的改进蚁群算 法及其应用. 北京科技大学学报, 2013, 35(7):955) [18] Tian Z J, Gao X H, Zhang M X. Path planning based on the improved artificial potential field of coal mine dynamic target navigation. J China Coal Soc, 2016, 41(Suppl 2): 589 (田子建, 高学浩, 张梦霞. 基于改进人工势场的矿井导航装置 路径规划. 煤炭学报, 2016, 41(增刊2): 589) [19] Chen E K, Wu M H, Zhang Y J. Path planning for coal mine rescue robot in complex environment. Coal Technol, 2018, 37(10): 301 (陈尔奎, 吴梅花, 张英杰. 复杂环境下煤矿救灾机器人路径规 划. 煤炭技术, 2018, 37(10):301) [20] Sun J, Chen Z H, Wang P, et al. Multi-region coverage method based on cost map and minimal tree for mobile robot. Robot, 2015, 37(4): 435 (孙建, 陈宗海, 王鹏, 等. 基于代价地图和最小树的移动机器人 多区域覆盖方法. 机器人, 2015, 37(4):435) [21] Hameed I A, Bochtis D, Sørensen C A. An optimized field coverage planning approach for navigation of agricultural robots in fields involving obstacle areas. Int J Adv Rob Syst, 2013, 10(5): 1 [22] Tang Q S. Application and research on tree structure based on depth-first algorithm. Comput Technol Dev, 2014, 24(9): 226 (唐青松. 深度优先算法在创建树形结构中的应用研究. 计算机 技术与发展, 2014, 24(9):226) [23] Luo C M, Yang S X, Li X D, et al. Neural-dynamics-driven complete area coverage navigation through cooperation of multiple mobile robots. IEEE Trans Ind Electron, 2017, 64(1): 750 [24] Yang S X, Meng M. An efficient neural network approach to dynamic robot motion planning. Neural Networks, 2000, 13(2): 143 [25] · 1228 · 工程科学学报,第 42 卷,第 9 期