Fuzzy Obstacle Avoidance and Navigation for Omnidirectional Mobile Robots

The motion planning and control problem is a well-known problem in the field of robotics. The objective is to find collision-free trajectories for a robot, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This paper describes an intelligent navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. Online planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the angular difference between the actual direction and position of the robot and the final configuration, to generate a new actuating command for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance in dynamic environments. The navigation system is being currently extended into an adaptive system using a neural network training algorithm for the rule-base. The presented approach to the obstacle avoidance problem has been influenced mainly by Khatib’s artificial potential fields method and the subsumption architecture developed by Brooks. The functioning of the fuzzy navigator with respect to omnidirectional mobile robots and results of simulated experiments are presented.