Path Allocation And Planning For Multiple Unmanned Land Vehicle System

With the development of intelligent robot technology, multiple unmanned ground vehicles (MUGV) research has progressed rapidly and sheds new light on many application fields. In a typical MUGV system, path allocation methods for multiple vehicles and the global planning algorithms for single vehicle have great effect upon the efficiency and robustness of integral system in that MUGV systems mainly depend on planning and cooperation scheme for their carrying into action.On a routing-oriented multiple robots task allocation paradigm, this thesis explores the path allocation problem of MUGV system at first. Three key techniques are probed in depth based on formal description of problem and definition of computation framework, including approximating the expected distance in the cyclic probability network by a normal form of shortest path with resource (SPR), solving multiple traveling vehicles problem by randomized approach, and updating the probability distributing for the stochastic environment.Global planning algorithms for single vehicle are discussed next. A modified dynamic programming algorithm is put forward to consider extra constraints as well as the basic requirement of minimizing the total routing cost. A marked example of those additional constraints is that the geometry of the vehicle must be carefully considered when planning the path. In practice, global path planner also should make vehicle travel in the middle of the road and likewise use as few waypoints as possible.At the last part of this thesis, experimental settings for above-mentioned framework and key algorithms are depicted. And the results of experiments illustrate that the framework and algorithms are essentially feasible and practical. Further researches are also suggested.