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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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