Path planning of mobile robot by mixing experience with modified artificial potential field method

In this article, a new method is proposed to help the mobile robot to avoid many kinds of collisions effectively, which combined past experience with modified artificial potential field method. In the process of the actual global obstacle avoidance, system will invoke case-based reasoning algorithm using its past experience to achieve obstacle avoidance when obstacles are recognized as known type; otherwise, it will invoke the modified artificial potential field method to solve the current problem and the new case will also be retained into the case base. In case-based reasoning, we innovatively consider that all the complex obstacles are retrieved by two kinds of basic build-in obstacle models (linear obstacle and angle-type obstacle). Our proposed experience mixing with modified artificial potential field method algorithm has been simulated in MATLAB and implemented on actual mobile robot platform successfully. The result shows that the proposed method is applicable to the dynamic real-time obstacle avoidance under unknown and unstructured environment and greatly improved the performances of robot path planning not only to reduce the time consumption but also to shorten the moving distance.