面向头颈部手术双臂机器人的防碰撞路径规划

Research on Anti-Collision Path Planning of Dual-Arm Robot for Head and Neck Surgery

针对在机器人辅助头颈部手术中双机械臂进行牵拉易产生碰撞且对目标软组织造成损伤问题,文中基于传统Informed-RRT*(Informed-Rapidly Exploring Random Tree*)路径规划算法叠加引力场降低路径搜索的盲目性。为优化传统Informed-RRT*路径规划算法存在导向性差和效率低等缺点,引入回归滤波机制避免搜索陷入局部最优,对步长进行动态调节。同时,优化了规划路径,采用冗余节点剔除策略,去除了冗余节点,提高了路径的平滑性。基于双机械臂碰撞检测方法和改进Informed-RRT*算法对双臂协调路径规划方法进行了研究。通过仿真实验可知,与原有算法相比,所提算法的迭代时间降低了72.76%,迭代次数降低了46.39%,平均路径长度约缩短6%,节点数约减少45%,验证了改进规划算法的有效性。

In view of the problem of collision and soft tissue injury caused by the traction of dual robotic arms in robot-assisted head and neck surgery,this study uses the traditional Informed-RRT*(Informed-Rapidly Exploring Random Tree*)path planning algorithm superimposes gravitational field to reduce the blindness of path search.In order to optimize the poor guidance and low efficiency of the traditional Informed-RRT*path planning algorithm,a regression filtering mechanism is introduced to avoid the local optimal search and dynamically adjust the step size.At the same time,the planning path is optimized and redundant node removal strategy is adopted to remove redundant nodes and improve the smoothness of the path.The dual-arm coordinated path planning method is investigated based on the dual-arm collision detection method and the improved Informed-RRT*algorithm.Simulation experiments show that compared with the original algorithm,the iteration time of the proposed algorithm is reduced by 72.76%,the number of iterations is reduced by 46.39%,the average path length is reduced by 6%,and the number of nodes is reduced by 45%,which verifies the effectiveness of the improved planning algorithm.