Omni-directional Wheels Robot Motion Control And Plan

Supported by the National Natural Science funds projects and the863National projects, this thesis discusses some issues about motion control and plan of omnidirectional mobile robots. Detailed work are as follows:1. A velocity compensation algorithm based on neural networks is proposed for omni-directional mobile robots. Through a comprehensive analysis of the omni-directional mobile robot kinematics and dynamics model, the executive deviation of robot is caused by the friction between the main wheel and the passive rollers. According to the results of the analysis model, A compensation algorithm based on a neural net-work model is presented. Experiment results and practical application show that the compensation model can improve the execution accuracy of the robot.2. A time optimal trajectory generation algorithm using bangbang control principal is proposed. Most research on trajectory generation of omnidirectional robots assume the final velocities to be zero in order to avoid discontinuities when approaching the destination. By introducing a new time optimal control method, our algorithm can handle the discontinuities when approaching the destination. With the new algorithm, our system is capable of generating very complex trajectories, for example, finding an optimal trajectory passing more than2points. Our algorithm can be used as a part of high-level path planner.3. A parameterized time optimal trajectory generation algorithm for omni-directional robots is proposed. The algorithm combines all of the special capability of omni-directional robots. It can make full use of the robots capability. The contrast experi-ments show that the trajectories generated by the parameterized algorithm are more smooth and efficiency than the one generated by the bangbang method. In some cases, the former is over10%faster than the latter. 4. For performing motion planning in complex environments that go beyond traditional navigation planning, the problem’s boundary conditions sometimes are not closed. An open boundary trajectory generation algorithm based on PSO(Particle Swarm Optimal) and neural network is proposed for such problem. We use PSO to do the offline optimal, and neural network to perform on-line application.