Mobile Robot Path Planning And Motion Controller

The mobile robot, an important branch of the field of robot, is of great interest because its wide use in various areas, such as industry, agriculture, military and education. In particular, path-planning is critical to mobile robot system because it determines the quality of the robot's task. As a result, path-planning has attained more and more attention in the field of mobile robot.Path-planning described in the thesis proposes assist robot to achieve the best path from starting point to goal point by avoiding all barriers. The path searching depends on one or more optimization rules, e.g., the lowest working cost, the shortest route and the shortest time.Firstly, we summarize and analyze the current advancement of mobile robot in several active areas. The status and the research method of path-planning are introduced with emphasis, and the significance and the contents of the research project are pointed out.Secondly, we develop a genetic simulated annealing algorithm, a hybrid of genetic algorithms and simulated annealing, by analyzing and comparing the advantages and disadvantages of genetic algorithm and simulated annealing method. The new algorithm has better capability of searching globally and locally, especially for the system with a large number of variables.In the following context, we use the genetic simulated annealing algorithm to carry out global path-planning in static. Before path-planning process, robot's working environment is built by vertex method. In the algorithm, a simple real number coding technique is used to accelerate the search of optimum path. The complicated two-dimensional path-coding problem is reduced to a simple one-dimensional problem in the coding scheme. Large-scale initialization combined with the selecting mechanism is applied to initialize the starting point out of the barrier area. The feasibility, smoothness and length of a path determinean efficient adaptive function. Select strategy uses proportion select method; crossover operator is non-symmetry one-point crossover strategy. Mutation operator firstly uses to heuristic mutation to transform all the paths to feasible paths; then the algorithm randomly selects a mutation point in each path and mutates the point at certainty probability Pm. In the simulated annealing, random moving rule uses Metropolis rule and devise efficient temperature update function. The simulation result confirms that the genetic simulated annealing algorithm is feasible and efficient for mobile robot path-planning.In addition, path-planning with dynamic obstacle is implemented by genetic algorithm. The dynamic algorithm is different from the static algorithm, i.e., the method needs to real-time decide a middle goal point. By considering the movement of obstacle, bumping, path length and path smoothness, the method also generates an efficient adaptive function in dynamic environment. The simulation of a typical dynamic system is carried out with VC++.Finally, we summarize the structure of robot's controller and propose an open robot controlling system based on CAN bus. The main parts of the system are described.