Real-time path planning is one of the most important issues of all mobile robot systems. These systems involve some kind of obstacle avoidance, starting from traditional algorithms that detect an obstacle and stop the robot in front of it, through complicated algorithms, that enable the robot to go around obstacles. Amongst such algorithms, potential fields are the core of a class of effective navigation schemes for autonomous robots. Despite its effectiveness, such schemes are limited by the fact that the robot can become trapped in local minima. This paper describes an effective algorithm to solve the local minima problem and oscillations in presence of obstacles. An evolutionary repulsive force, which relies on a virtual obstacle in arrangement with a potential field based algorithm, is presented. The optimal position of the virtual obstacle which ensures the optimal steering angle is also presented. Comparing this algorithm with most other algorithms, one can see that our proposal has several advantages. The first one is that this proposal can be used for path planning in a completely known or unknown environment. The second one is that the size of the robot is taken into consideration rather than treating the robot as a point. The final one is that the robot follows the optimal steering angle and hence time saving. The convergence of the proposal is discussed to guarantee its reliability and the simulation results have proved its feasibility and validity. |
