| As a kind of robot with manipulability and mobility, mobile manipulator system has drawn tremendous attention in many domains in the past decade, such as industry, military and deep space. Motion planning, decided and restricted by the task, is an important precondition for the applications of the mobile manipulator. This thesis focuses on several problems in the motion planning of the mobile manipulator, such as point to point motion planning of mobile platform, path planning in the target search task and the inverse kinematics.Contributions of this thesis can be summarized as follows:1. Architecture is used to define the relationships among function modules, decide the execution model of tasks, and works as a frame for carrying certain theories and technologies of mobile manipulator. In this study, several classical architectures are reviewed, and then, a hybird architecture is designed according to the characteristics of task executed by the mobile manipulator. This hybird architecture is employed to provide guidance for further detailed studies.2. Path planning for mobile platform is the foundation of motion planning for mobile manipulator. Considering the problems of artificial potential field (APF) and the influence of its parameters, a new version of APF is presented to improve the quality of the generated path and reduce the probability of appearing local minimum. This improvement is realized by optimizing several parameters using quantum genetic algorithm. To solve the matching problem between APF functions and obstacles, a novel method is proposed for describing obstacles, in which it is assumed that point charges distribute uniformly along the boundaries of obstacles. According to the above theories, a hierarchical path planning strategy based on grid-geometric map is proposed. In this planning strategy, the local minimum problem is avoided and computational consumption of A* is decreased by the cooperation between APF and improved A*.3. In the target search task with a mobile manipulator, there exist loops in most complicated environments. We found that different directions in loops lead to different expected-time, and no studies so far have been made to solve this problem. Therefore, a direction choosing method is presented, and applied to a heuristic algorithm to reduce its expected-time for target search. This improved heuristic algorithm is employed to plan paths for target search in probabilistic environments. Simulation and experiments demonstrated that this approach can reduce the expected time for target search.4. Considering the sequence planning problem in target search task of mobile manipulator, a novel map transformation framework (MTF) is proposed. With MTF, a feature map or a topological map can be converted into a standard topological map on which many graph search algorithms suitable for Chinese Postman Problem (CPP) can be employed to carry out path planning for multiple target search. Similar to some heuristic algorithms, the MTF-based algorithm is globally optimal and has polynomial order time complexity. Theoretical analysis and experiments all indicate that the route generated by the MTF-based algorithm is better than those planned by several other target search algorithms.5. To deal with the global motion planning of redundant mobile manipulator, a general method is proposed for the mobile platform with a planar manipulator. In this method, the model of this kind of mobile manipulator is constructed, the approach discussed in chapter 3 is applied to plan path for mobile platform, and an improved particle swarm optimization (PSO) algorithm is used to solve the inverse kinematics of manipulator. This general method is applied to do motion planning for a robot with a six-link manipulator, and then tested with a simulation platform built by us. Simulation shows that the proposed method can be used to plan a path for the robot and calculate inverse kinematics for this manipulator.6. A novel immune clonal algorithm, called the immune clonal algorithm based on biological information (ICABBI), is proposed and used to solve the inverse kinematics of complex manipulators. In ICABBI, the computational implementation of the clonal selection principle takes into account environmental information, cell history information and hereditary characteristics. This mechanism realizes information communication in whole artificial immune systems. We tested ICABBI with several classical global optimization problems to analyze its universality. Then, the ICABBI is applied to solve the inverse kinematics of a 6-DOF manipulator. ICABBI has some features that are unique among biology-based methods. It can deal with the some complex nonlinear global optimization problems, such as the inverse kinematics of complex manipulators, and has satisfactory performance on some high-dimensional problems. |
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