Researches On Technologies For Robot Path Planning Based On Artificial Potential Field And Genetic Algorithm

Mobile robot system, as an integrative subject with updated research results in mechanical, electronic, computer, automatic control and artificial intelligence, represents is tremendous success of mechatronics. In recent years, mobile robot path planning has become an important research area in automatic control, computer and artificial intelligence. The development of mobile robot path planning has imposing on the defense, society, economy and academy, and becomes the tactic research object of high technology of all countries.Mobile robot path planning based on artificial potential field and genetic algorithm has been studied in this paper. Several improved methods and novel solutions are presented in order to improve computational efficiency, and additionally extend application domains. The main content of this dissertation include the following aspects:(1) An improved method for moblie robot path planning is proposed for the innate limitations of potential field. In order to pretreat and optimize the workspace of robot, expansion and erosion mathematical operations based on mathematical morphology of binary image are used to integrate discrete obstacles in to complete obstacles, and improved potential field is used to navigate robot to improve trajectory of robot. Furthermore, sub-goal point is set for robot to get rid of local minimization rapidly.(2) Each object is probably moving in a dynamic environment for mobile robot path planning, so the position, the velocity and the acceleration are taken into account, the relative factor of the distance and the acceleration is introduced in the repulsive potential function. In path planning when the robot reaches or catches up the goal, the relative acceleration is zero. Otherwise, the parameters are adjusted to reduce the relative acceleration to zero. In addition the potential field method is combined with the genetic algorithm to plan the path for mobile robot, an escape force function is taken into account in potential function, so the local minimas is solved by the escape force function. Genetic algorithm is used for global search to get the optimal path, and guarantees that the best individual is passed to the next generation.(3) An improved genetic algorithm is presented for mobile robot path planning in static environment. In this mothod the fitness function is introduced by tacking the path length, the penalty factor of collision and the path clearance into account, a set of suitable genetic operators are designed, the path repair mechanisms are used for a local genetic search and the optimization parameters are given for the desired path. The proposed mothod can accomplish search and find a optimal path from the start to the goal.(4) Based on artificial potential field and grid method, in order to solve the prematurity and lower convergence speed in genetic algorithm for robotic path planning, a novel mobile robot path planning method based on quantum genetic algorithm is proposed. In the method the quantum genetic algorithm is combined with artificial potential field and grid method to plan parh. it uses grid method to establish mobile robot work environment model, artificial potential field to control mobile robot, quantum genetic algorithm to select the optimal or sub-optimal path, and double fitness evaluation function to evaluate the path to protect the optimal or sub-optimal path in to the next generation.(5) As premature convergence of genetic algorithms can make some outstanding individuals to be excluded prematurely, and lead to narrowing the search range and causing the local optimization. In order to overcome this defect, an improved chromosome encoding based adaptive genetic algorithm is proposed. In this algorithm, pairs of direction and distance used for chromosome encoding is combined with adaptive adjusting probability functions(P and Pm) for genetic operator, the method alleviates the problem of premature convergence and improves the efficiency and range of searching.(6) A fusion algorithm based on quantum chromosome mutation is proposed. Firstly, the new repulsive potential function are reformed. Then, the fusion method of artificial potential field and grid is used to establish work environment model for mobile robot and produce initialize population. Finally, quantum bit is used to code chromosome, and quantum chromosome mutation is used to update individual of population for getting the best path. This method increases population quality and convergence rate, avoids safely the obstacles by optimal path, it is fit for the solution of complex optimization problems.A summary of the research conclusions and a discussion on the most promising paths of future research work are presented in the last chapter of this dissertation.