Research On Dynamic Path Planning For A Mobile Robot

Robot development entered the third generation- intelligent robot era, with independent decision - making ability of mobile robot is a hotspot in the research of the Robotics. The key issues of Mobile robot research include: the robot structure, system structure, path planning and feedback stabilization, etc, the path planning of mobile robot an important issue.The current Mobile robot path planning method is mainly directed against the static path planning method, dynamic path planning study is still in the initial stage. The main dynamic path planning method mainly include: artificial potential field Method and genetic algorithms and neural networks. In this thesis,on the analysis of domestic and international path planning, on the basis of summarizing current dynamic path planning method, the advantages and disadvantages of the proposed based on Quantum Particle Swarm Optimization Algorithm of artificial potential field method for mobile robot dynamic path planning methods.The phases of this paper are guided as follows:Mathematical model based on artificial potential field Method and path - planning simulation test and analysis of simulation test results defects in artificial potential field Method; for dynamic environments, the introduction of relative velocity potential field, on algorithm simulation and simulation of dynamic environment for path planning of potential field method parameters effect;Introduction of quantum particle swarm optimization algorithm to parameter optimization of potential field method, based on quantum particle swarm optimization algorithm based on artificial potential field Method improvement of the traditional potential field defects in, according to the environmental effect of information balance of potential field parameters. Simulation experiment prove the algorithm in dynamic environment effectively;Finally, in order to facilitate the operation, Human -Computer interface is designed on the basis of MATLAB/GUI simulation platform. To prove the effectiveness and universality of algorithm, Users can overwrite the callback function according to requirement of the environment information variable.