| Summary: | 碩士 === 國立政治大學 === 資訊科學學系 === 91 === The automatic generation of crowd motions can be used in planning the path of many mobile robots and in simulating the motions of virtual humans in computer animation. In the literature, there exist two categories of approaches to this problem: decoupled and centralized approaches. The decoupled approach divides the planning problem into several sub-problems, each of which is for a robot. In this thesis we have used a prioritized planning approach with an artificial potential field and the A* search algorithm to solve each sub-problem in a given order. This decoupled approach usually is not complete because later planning must be under the constraint of previous planned results. On the other hand, the centralized approach considers the configurations of all robots and can be made complete by searching the composite configuration space. In this thesis, we use the randomized path planner (RPP) with a potential field as an example of the centralized approach. However, this planner is not very efficient for a large number of robots because of frequent inter-collisions between robots. Therefore we propose a hierarchical dynamic grouping method to improve the centralized RPP method. The robots are organized as groups enclosed by a sphere tree structure that can split or merge dynamically according to the environment. The robots in the same group always move with the same direction. Consequently the collisions between robots decrease significantly during the search and the planning efficiency is greatly improved. We have designed extensive experiments to compare the performance of the decoupled approach, the centralized approach and the dynamic grouping method. We also analyze these approaches in various scenarios in order to illustrate their tradeoffs. In addition, we have designed a path-smoothing method and apply the planning result to a production process of computer animation.
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