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在巡视器停泊就位探测路径规划问题的研究中,要求在近距离、无法再栅格化高程图的情况下,规划出行驶到探测点的路径和具体的机械臂探测步骤,以增强巡视器自主探测能力,减少数传和遥测资源的浪费。所以深度挖掘点云数据中的地理信息,合理估计巡视器停泊位置位姿,解决探测任务中需满足的光照、平整度、机械臂控制指令数最少的约束方法,达到近距离路径规划优化。为解决上述问题,提出建立机械臂正逆运动学模型,为机械臂运动规划和工作空间求解奠定基础。其次,通过点云数据中的地理位置信息和星历计算得到的光照条件对探测点进行评估,选出满足X质谱仪工作距离和探测光照约束的探测点,通过探测点位置和机械臂的工作空间,计算可能的停泊位置并估计巡视器的停泊姿态。最后通过SA*算法规划出满足位形切换次数最少约束的机械臂展开序列。通过巡视器内场实验结果表明巡视器停泊就位探测路径规划能够自主规划出探测步骤,不仅满足精度要求和探测任务中的约束条件,还极大的提高了探测效率。
In the study of the parked-in-place probing path planning problem, it is required to plan the path to the probing point and the specific robotic arm detecting steps at a short distance and unable to rasterize the elevation map, so as to enhance the inspectors Self-detection capabilities to reduce the waste of data and telemetry resources. Therefore, the geographic information in the point cloud data is deeply tapped and the pose of the berth position is reasonably estimated to solve the constraint method of least illumination, smoothness and arm control commands needed in the exploration mission, and the optimization of short-range path planning is achieved. In order to solve the above problems, it is proposed to establish the positive and negative kinematics model of manipulator, which will lay the foundation for the motion planning and working space solution of manipulator. Secondly, the detection points are evaluated by the geographic location information in the point cloud data and the light conditions calculated by the ephemeris, and the detection points that satisfy the working distance of the X-ray mass spectrometer and the detection light constraints are selected. Through the positions of the detection points and the work of the robot arm Space, calculate possible parking locations and estimate the parked mooring attitude. Finally, the SA * algorithm is used to design a robot deployment sequence that satisfies the least constraint of the number of configuration changes. The experimental results of the inner field of the patrol show that the patrolling parking-in-place path planning can independently plan the detection steps, which not only meets the precision requirements and the constraints in the exploration tasks, but also greatly improves the detection efficiency.