Path Planning For Mobile Robots Based On Quantum-behaved Partice Swarm Optimization

Mobile robotics is a major application of cutting-edge technology. Related technologies in the mobile robot research, path planning is an important part of the study and issues.amongalloftherobotiesresearches.The chief task of the path-planning is: when the mobile robot is running under the environment space with obstacle it is usually asked to seek one of optimal path which would link between the start point and the end point,and make sure in the motion of this process,mobile robot can pass all obstacle in safe and collison-free.Particle Swarm Optimization (PSO) is an important branch of swarm intelligence, PSO is realized by the organic social behaviors instead of the mechanism of natural selection in Evolutionary Computation, and by the cooperation and competition among the individuals themselves to search the optimum of the problem. PSO has been widely applied in image processing, pattern recognition, operational research and so on due to its simple concept, simplicity of implementation, less parameters to control and rapid convergence speed.However, Van de Bergh has proved that PSO does not satisfy the requirements of a global search algorithm and PSO cannot guarantee to converge on the global minimum. Aiming at the fatal limitation, keeping to the philosophy of PSO algorithm, the Quantum-behaved Particle Swarm Optimization (QPSO) has been proposed by Sun, which introduced the concept of quantum, built a Delta potential well model to simulate the learning inclination of particles and designed a method of controlling the parameters on global level. As the benchmark functions shown, QPSO has better performance than PSO.Firstly, the background and significance of the study are described in this paper. And then the Evolutionary Algorithm (EA) and Swarm Intelligence related to QPSO algorithm are introduced and the differences and similarities among QPSO, PSO are compared, which show the advantage of QPSO and the necessity of this research.And its widespread applications have been introduced.The origin and the development of mobile robot are also outline in this paper.The mobile robot's prospect aspect in the future-intelligent robot has been presented.The path planning for mobile robot is the most important aspect of interlligent robot.The general conceptions,characteristic,classify bassed issue and some familiar methods of path palnning are presented.In this paper,according to the characteristics of mobile robot path planning,proposed a new method for mobile robot path planning is the Quantum-behaved Particle Swarm Optimization .Meanwhile , some invalid particles will be change into random valid particles once again.So expand your search range and keep part from get into local optimal.The path planning for mobile robots based Quantum-behaved Particle Swarm Optimization includes two steps: The first step is to establish a free-space mobile robot model, the second step is adopting the quantum-behaved particle swarm optimization algorithm to fing out the global optimal path . The computer simulation experiment was carried out, by comparing the results confirmed that the method proposed by this paper, both in convergence rate, or in a dynamic convergence characteristics than the particle swarm optimization, as well as other planning algorithm of mobile robot global path better planning methods. Finally, conclusions are given with recommendation for future work.