Intelligent omni-directional vision-based mobile robot fuzzy systems design and implementation

An evolutional particle swarm optimization (PSO)-learning algorithm is proposed to automatically generate fuzzy decision rules. Due to the development of the fuzzy rule-based system, it actually regulates the omni-directional vision-based mobile robot for obstacle avoidance and desired target approximation as soon as possible. In the proposed image processing algorithm, an image direct transformation method is applied to convert the omni-directional scene into panoramic normal-view. Thus, the objects positions of obstacle and target are detected by the proposed color image segmentation. Human knowledge-based fuzzy systems demonstrate their well adaptability for nonlinear and time-variant features of the mobile robot to actually approach the desired location whatever it is surrounded in a known or unknown environment. In software simulations, the omni-directional mobile robot can move toward desired targets from different initial positions and various block sizes. In hardware implementations, the fuzzy control system embedded in actual mobile robot platform is used to real-time manipulate the omni-directional wheels through the motor drivers by the captured image positions of the obstacle and target. The selected fuzzy rules are efficient to control the direction and speed of omni-directional wheels to achieve the desired targets.