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摘要: 双足步行机器人的足迹规划方法难以满足快速步行条件下的计算效率要求, 并存在步幅变化时运动失稳的风险, 2D环境下点机器人栅格规划则难于生成针对双足步行的高效路径.本文提出针对各向异性特征全方位步行机器人的一种路径规划策略, 将状态网格图方法拓展到全方位移动机器人领域, 基于三项基本假设及基元类型划分给出了系统的运动基元枚举及选择方法, 借助实时修正的增量式AD*搜索算法实现仿人机器人在动态环境下的快速路径规划, 通过合理选择启发函数及状态转移代价, 生成了平滑高效的路径, 为后续足迹生成的动力学优化提供了基础.计算机仿真证实了方法对各类环境的适应性, Robocup避障竞速挑战赛的成功表现证明了方法对于机器人样机部署的可行性及其提高步行效率的潜力.Abstract: Footstep planning for bipedal walking robot is difficult to fulfil the requirement of computational efficiency at high walking speed; it also suffers from the risk of falling over with stride variations. On the other hand, 2D grid-based planning strategy for point robots is unable to generate an efficient walking path for bipedal robots. A path planning approach for heterogeneous omnidirectional bipedal walking robot is proposed in this paper. State lattice graph is brought into the omnidirectional moving condition. Based on three assumptions and type classification, a systematic motion primitive enumerating and selecting method is given. The rapid path planning for humanoid robots in dynamic environment can be achieved by making use of the algorithm of anytime repairing incremental search AD*. The smooth and efficient path, generated by reasonable heuristics function and state transfer cost, provides the foundation for dynamics optimized footstep planning. Simulation has demonstrated the adaptability of the biped robot in various environments. The successful performance in obstacle avoidance challenge in Robocup proves the possibility of implementation on physical robots and the capacity for walking efficiency improvement.
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Key words:
- Humanoid robot /
- path planning /
- state lattice /
- dynamic planning /
- footstep planning
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图 1 基于栅格地图规划及曲线路径规划的对比
Fig. 1 Comparison of grid map based planning and curved pathplanning
图 3 基于Lattice网格图的路径规划原理图
Fig. 3 Illustration of the state lattice graph based pathplanning
图 4 前进、侧移、旋转一个栅格单位的基本运动单元
Fig. 4 Basic motion primitives including forward walking, sidling and self-spin for one unit
图 5 始末姿态角关系与光滑路径生成示意图
Fig. 5 Illustration of the relationship between the start-endattitude angle and the smooth path generation
图 7 由基本单元和第一类单元生成第二类单元示意图
Fig. 7 Generation of motion primitives of the second classfrom the basic and the first class
图 9 运动基元代价及步幅过渡代价示意图
Fig. 9 Illustration of the motion primitive cost and statetransferring cost
图 10 忽略与考虑运动基元过渡代价的规划结果对比
Fig. 10 Comparison of planning results with ignoring and usingmotion primitive transferring cost
图 11 路径规划与足迹规划的结果对比
Fig. 11 Comparison of result with path planning and footstepplanning
图 12 特定环境下的路径规划和步行跟随结果
Fig. 12 Path planning and path following results in the specified environments
图 13 MOS-Strong仿人机器人外形及控制系统原理图
Fig. 13 Appearance of MOS-Strong humanoid robot and theschematic of its control system
图 15 仿人机器人在Robocup避障竞速中的连拍照片
Fig. 15 Snapshots of the humanoid robot in the obstacleavoidance challenge of Robocup
图 16 机器人在Robocup避障竞速中步行与环境感知重现
Fig. 16 Recovery of walking steps and environment perceptionof the robot during the obstacle avoidance challenge in Robocup
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[1] Spong M W, Hutchinson S, Vidyasagar M. Robot Modeling and Control. Hoboken, NJ, USA:John Wiley and Sons, 2006. [2] Latombe J C. Robot Motion Planning. Berlin, Germany:Springer, 1991. [3] Choset H, Lynch K M, Hutchinson S, Kantor G A, Burgard W, Kavraki L E, Thrun S. Principles of Robot Motion:Theory, Algorithms, and Implementations. Cambridge, UK:A Bradford Book, 2005. [4] Lavalle S M. Planning Algorithms. Cambridge, USA:Cambridge University Press, 2006. [5] Xia Z Y, Xiong J, Chen K. Global navigation for humanoid robots using sampling-based footstep planners. IEEE/ASME Transactions on Mechatronics, 2011, 16(4):716-723 doi: 10.1109/TMECH.2010.2051679 [6] Hornung A, Maier D, Bennewitz M. Search-based footstep planning. In:Proceedings of the 2013 ICRA Workshop on Progress and Open Problems in Motion Planning and Navigation for Humanoids. Karlsruhe, Germany:IEEE, 2013. [7] Xia Z Y, Chen G D, Xiong J, Zhao Q F, Chen K. A random sampling-based approach to goal-directed footstep planning for humanoid robots. In:Proceedings of the 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Singapore:IEEE, 2009. 168-173 [8] Xia Z Y, Xiong J, Chen K. Parameter self-adaptation in biped navigation employing nonuniform randomized footstep planner. Robotica, 2010, 28(6):929-936 doi: 10.1017/S0263574709990804 [9] Liu H, Sun Q, Zhang T W. Hierarchical RRT for humanoid robot footstep planning with multiple constraints in complex environments. In:Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura:IEEE, 2012. 3187-3194 [10] Huang W W, Kim J, Atkeson C G. Energy-based optimal step planning for humanoids. In:Proceedings of the 2013 IEEE International Conference on Robotics and Automation. Karlsruhe:IEEE, 2013. 3124-3129 [11] Perrin N, Stasse O, Baudouin L, Lamiraux F, Yoshida E. Fast humanoid robot collision-free footstep planning using swept volume approximations. IEEE Transactions on Robotics, 2012, 28(2):427-439 doi: 10.1109/TRO.2011.2172152 [12] Maier D, Lutz C, Bennewitz M. Integrated perception, mapping, and footstep planning for humanoid navigation among 3D obstacles. In:Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Tokyo:IEEE, 2013. 2658-2664 [13] 张继文, 刘莉, 李昌硕, 陈恳.仿人机器人参数化全方位步态规划方法.机器人, 2014, 36(2):210-217 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201402011.htmZhang Ji-Wen, Liu Li, Li Chang-Shuo, Chen Ken. Parametric Omni-directional gait planning of humanoid Robots. Robot, 2014, 36(2):210-217 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201402011.htm [14] Yoshida E, Esteves C, Belousov I, Laumond J P, Sakaguchi T, Yokoi K. Planning 3-D collision-free dynamic robotic motion through iterative reshaping. IEEE Transactions on Robotics, 2008, 24(5):1186-1198 doi: 10.1109/TRO.2008.2002312 [15] Hornung A, Bennewitz M. Adaptive level-of-detail planning for efficient humanoid navigation. In:Proceedings of the 2012 IEEE International Conference on Robotics and Automation. Saint Paul:IEEE, 2012. 997-1002 [16] Sprunk C, Lau B, Pfaffz P, Burgard W. Online generation of kinodynamic trajectories for non-circular omnidirectional robots. In:Proceedings of the 2011 IEEE International Conference on Robotics and Automation. Shanghai, China:IEEE, 2011. 72-77 [17] Pivtoraiko M, Knepper R A, Kelly A. Differentially constrained mobile robot motion planning in state lattices. Journal of Field Robotics, 2009, 26(3):308-333 doi: 10.1002/rob.v26:3 [18] Likhachev M, Ferguson D. Planning long dynamically feasible maneuvers for autonomous vehicles. The International Journal of Robotics Research, 2009, 28(8):933-945 doi: 10.1177/0278364909340445 [19] 张浩杰, 龚建伟, 姜岩, 熊光明, 陈慧岩.基于变维度状态空间的增量启发式路径规划方法研究.自动化学报, 2013, 39(10):1602-1610 doi: 10.3724/SP.J.1004.2013.01602Zhang Hao-Jie, Gong Jian-Wei, Jiang Yan, Xiong Guang-Min, Chen Hui-Yan. Research on incremental heuristic path planner with variable dimensional state space. Acta Automatica Sinica, 2013, 39(10):1602-1610 doi: 10.3724/SP.J.1004.2013.01602 [20] Bertsekas D P. Dynamic Programming and Optimal Control (Third edition). Belmont, Mass:Athena Scientific, 2005. [21] Kavraki L E, Svestka P, Latombe J C, Overmars M H. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 1996, 12(4):566-580 doi: 10.1109/70.508439 [22] Lavalle S M. Randomized kinodynamic planning. The International Journal of Robotics Research, 2001, 20(5):378-400 doi: 10.1177/02783640122067453 [23] Likhachev M, Gordon G J, Thrun S. ARA*:anytime A* with provable bounds on sub-optimality. In:Advances in Neural Information Processing Systems. Massachusetts:MIT Press, 2003. 767-774 [24] Likhachev M, Ferguson D, Gordon G, Stentz A, Thrun S. Anytime search in dynamic graphs. Artificial Intelligence, 2008, 172(14):1613-1643 doi: 10.1016/j.artint.2007.11.009 [25] Likhachev M. Sbpl ROS wiki[Online], available:http://wiki.ros.org/sbpl, June 30, 2015. -
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