2016年10月19日，我国的天宫二号与神舟十一号飞船对接成功，宇航员与空间机械臂进行了国际首次人机协同在轨维修科学试验，人机协同在轨维修已成为天宫二号三大关键试验任务之一。未来我国空间站的建设、在轨维护、科学实验同样也需要一款空间机械臂来辅助宇航员工作。空间环境中的机械臂路径规划问题研究对实现空间机器人与宇航员协作完成在轨任务具有重要意义。

现如今，国内外对于空间机械臂的相关研究尚处于起步的阶段，仍然存在许多关键问题需要解决。空间机械臂在动态、不确实的空间站环境下与宇航员协作完成在轨任务时，协作机械臂不仅会受到关节限位、自碰撞等固有约束的影响，还会受到保持特定位姿等任务约束的影响；同时还需要保持拟人构型以增强人机交互的舒适性。这对本已复杂的仿人机械臂高维运动规划提出了更高的要求，主要涉及到任务约束下的路径规划以及运动拟人化两个关键问题。

本研究课题以七自由度协作机械臂为研究对象，以任务约束下协作机械臂拟人避障运动规划方法、动态环境下基于该方法框架的避障运动规划为研究主线，逐步开展动态环境下协作机械臂拟人避障运动规划的研究探索，具体研究内容如下：

对于任务约束下协作机械臂拟人避障运动规划部分，针对现有方法将仿人机械臂任务约束路径规划问题和运动构型拟人化问题单独考虑，不能实现协作机械臂在满足约束下运动的同时实现其运动拟人化的问题，提出了一种任务约束下基于人臂运动学习的协作机械臂拟人运动规划方法，该方法直接在机械臂满足约束的任务空间采用随机采样规划算法规划出满足协作机械臂固有约束以及任务约束的末端路径；建立包含上下臂长的高斯过程回归模型对人臂运动进行学习映射给仿人机械臂，得到拟人臂构型；对采用基于臂角参数的逆运动学求解无效采样点的完整零空间，选取次拟人臂构型。该方法（1）能够同时解决仿人机械臂的任务约束运动规划和运动拟人化问题；（2）建立人臂高斯过程回归模型，实现随机采样规划算法直接在满足约束的任务空间进行采样，避免采用约束满足的方法，提高了算法规划效率；（3）将上下臂长也作为模型的输入，加入到模型的训练，避免协作机械臂模型改变导致的数据重新采集和训练。（4）对自运动流形进行了完整描述，保证了规划算法的概率完备性。

对于动态环境下避障运动规划部分，本文在上述规划方法的基础上，分析了末端位姿空间中全局路径规划与局部路径规划算法的优劣，针对全局路径规划算法不能很好地解决动态环境中机械臂避障路径规划问题以及局部规划算法不能得到最优解且极易陷入局部最小值的问题，提出一种结合了RRT*规划算法和人工势场法的混合算法，该算法（1）全局规划阶段忽略动态障碍物采用RRT*算法得到一条渐进最优规划路径，以该全局路径作为局部规划器的路径引导；（2）局部规划阶段采用改进人工势场法，该改进势场法是在传统位置势场的基础上加入速度、加速度以及全局路径指引势场，使规划器充分利用全局环境信息，实现动态环境下的实时避障的同时能沿着渐进最优路径行走，极大避免陷入局部最小值的问题。

On October 19, 2016, China's tiangong-2 and shenzhou-11 were successfully docked. Astronauts and space manipulators carried out the first international man-machine coordinated on-orbit maintenance scientific experiment. The man-machine coordinated on orbit maintenance has become one of the three key test tasks of tiangong-2. In the future, China's space station construction, on-orbit maintenance and scientific experiments will also need a space manipulator to assist astronauts. The research on the path planning of the manipulator in the space environment is of great significance for space robot and astronaut to cooperate to complete the on-orbit mission.

Nowadays, domestic and foreign research on space manipulator is still in the initial stage, and there are still many key problems that need to be resolved.  When a space manipulator cooperates with astronauts to complete on-orbit tasks in a dynamic and uncertain space station environment, the collaborative manipulator will not only be affected by inherent constraints such as joint limit and self-collision, but also be affected by tasks constraints such as maintaining a specific posture. At the same time, it needs to maintain the anthropomorphic configuration to enhance the comfort of human-computer interaction. This puts forward higher requirements for the already complex high-dimensional motion planning of collaborative manipulator, which mainly involves two key problems: Path Planning under task constraints and motion anthropomorphization.

In this research topic the 7-DOF collaborative manipulator is taken as the research object, the anthropomorphic obstacle avoidance motion planning of collaborative manipulator under task constraints and the obstacle avoidance motion planning based on the method framework in a dynamic environment are taken as the main research lines. Gradually carry out the research and exploration of the anthropomorphic obstacle avoidance motion planning of the collaborative manipulator under the dynamic environment. The specific research content is as follows:

For the part of anthropomorphic obstacle avoidance motion planning of collaborative manipulators under task constraints, the path planning problem of task constraints and the motion configuration anthropomorphic problem separately according to the existing methods, which cannot realize the motion anthropomorphization of collaborative manipulators while meeting the constraints. A learning-based anthropomorphic motion planning method for a collaborative manipulator under task constraints is proposed. This method uses random sampling planning algorithm to plan the end path of collaborative manipulator which meets the inherent constraints and task constraints in the task space where the manipulator meets the constraints directly; establishes Gaussian process regression model including the upper and lower arm length to learn and map the human arm motion to the collaborative manipulator, and obtains the anthropomorphic arm configuration; adopts inverse kinematics based on arm angle parameters for the collaborative manipulator; adopts inverse kinematics based on arm angle parameters to solve the complete null space of invalid sampling points, and selects Sub-anthropomorphic arm configuration. This method (1) can simultaneously solve the task constraint motion planning and motion anthropomorphization problems of the collaborative manipulator; (2) establish the Gaussian process regression model of collaborative manipulator, and realize the random sampling planning algorithm to directly sample in the task space which meets the constraints, so as to avoid the constraint satisfaction method, and improve the efficiency of algorithm planning; (3) take the upper and lower arm length as the input of the model and adds it to the training of the model to avoid the data re-collection and training caused by the change of the collaborative manipulator model. (4) describe the self-motion manifold completely to ensure the probability completeness of the planning algorithm.

For the part of obstacle avoidance motion planning in dynamic environment, based on the above part of the planning method, this paper analyzes the advantages and disadvantages of global path planning algorithm and local path planning algorithm in the manipulator tip constraint space. Aiming at the problem that the global path planning algorithm planning algorithm cannot solve the obstacle avoidance path planning problem of the manipulator in a dynamic environment, and the local planning algorithm cannot get the optimal solution and easily fall into the local-minimum, a hybrid algorithm combining the RRT* algorithm and the artificial potential field method is proposed. The algorithm (1) ignores the dynamic obstacles in the global path planning stage, uses RRT * algorithm to obtain an asymptotically optimal planning path, and takes the global path as the path guidance of the local planner; (2) The improved artificial potential field method is used in the local planning stage. The improved potential field method is improved by adding velocity, acceleration and global path guidance potential field on the basis of the traditional position potential field, so that the manipulator can make full use of the global planning to avoid obstacles in real time and walk along the asymptotically optimal path in dynamic environment.

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