Swarm Intelligence Algorithm And Its Application In The Path Planning And Tracking Control Of Mobile Robot

Swarm Intelligence(SI) is the property of a system whereby the collective behaviors of agents interacting locally with their environment.SI provides a basis with which it is possible to explore collective problem solving without centralized control or the provision of a global model.SI algorithm is a kind of heuristic search method that can solve the specified problems based on collective behaviors.The characteristic of SI algorithm is stochastic,parallel and distributed.Particle Swarm Optimization(PSO) algorithm is one of the typical SI algorithms which is developed by Kennedy and Eberhart in 1995.Since PSO algorithm was developed,it has attracted many researchers in the fields concerned as its characteristics of simple computation,easy realization and few parameters.J.Sun proposed Quantum-behaved Particle Swarm Optimization(QPSO) algorithm based on the deep study of PSO algorithm and inspired by quantum physics.QPSO algorithm has much fewer parameters and much stronger global search ability than the PSO algorithm.Several improved QPSO algorithms with different search strategies are proposed base on QPSO algorithm.QPSO algorithm by binary encoding(BQPSO) is proposed for solving discrete problems,and is given deep analysis and study on it.The applications of SI in the path planning and tracking control of mobile robot are also studied in the work.The main contents of this dissertation are as follows:(1) Research background of SI algorithms and path planning,tracking control of mobile robot are expatiated.The current research situations of two typical SI algorithms are detailed introduced,which are Ant Colony Optimization(ACO) algorithm and PSO algorithm.An introduction of design of path planning and tracking control of mobile robot is presented.Research methods and ideas in the work are proposed.(2) In order to maintain the diversity of the population and enhance global search ability of the quantum-behaved particle swarm optimization,an improved QPSO algorithm with the best individual of random selection is proposed to avoid the swarm diversity getting into a low level.The improved algorithm shows preferable ability in solving the multi-modal problems.(3) Decision-making rights are different according to the individuals' fitness values by the analysis of SI decision-making mechanism.Improvements of QPSO algorithm are proposed to improve searching efficiency and optimal performance by introducing linear weighted operator and elitist selection operator into algorithm.Two improved QPSO algorithms show good performance by tested them on the benchmark functions and the QPSO algorithm with linear weighted operator is better.(4) The thought of BQPSO is discussed and evolution equations are given which are completely different from the QPSO algorithm.The convergence of BQPSO algorithm is analyzed through functional analysis and stochastic process method,and conclusion of global convergence is drawn.BQPSO algorithm shows better performance than BPSO algorithm in solving test functions.(5) The parameter of an algorithm is the key issue that affects the algorithm's performance and efficiency.The methods for taking value of control coefficient in BQPSO algorithm are analyzed systemically.Three strategies are proposed,including:setting the parameter as a fixed value,making the parameter take value linearly of nonlinearly according to the iteration,and letting the value adaptively according to the evolution results.Some guiding conclusions are summarized.An improvement on BQPSO algorithm is proposed to avoid the swarm diversity getting a low level based on diversity measure,the improvement is realized by resetting average best value of all particles and mutating the swarm's global best particle.(6) Path planning method of robot that is combined BQPSO algorithm and grids technologies is proposed by base on the analysis of different path planning technologies.The design result of this method is validated by a number of design examples.Simulation results show that collision-free paths can be planned by this method in robot working space.(7) Two controller design methods of robots trajectory tracking are analyzed.Trajectory tracking controller is designed according to back-stepping approach.The basic tenet of sliding mode is analyzed and new sliding mode control law is designed by combining index reaching law and power reaching law.The parameters in back stepping tracking controller and sliding mode tracking controller are optimized through PSO algorithm,QPSO algorithm and improved QPSO algorithm.The design result of optimal tracking controller is validated by a number of design examples.It shows that two tracking controllers can control robot to track planned trajectory.Simulation results show that QPSO algorithm and its improvement can get better effect in optimizing parameters of controllers.(8) An integrated example shows that the BQPSO algorithm applied in path planning and improved QPSO algorithm applied in design of tracking controller is effective and feasible.It proposes a new method for the design of integrated system with path planner and tracking controller.The main contributions in this work are summarized at last and further research considerations and put forward.