Evolutionary Computation Applied to the Control of a Robotic Manipulator with Obstacle Avoidance

The successful manipulation of a robotic arm is crucial in industry, in surgical fields, or in space exploration. Many previous studies on evolutionary or genetic algorithms successfully determined effective configurations of arm joints in order to reach a specific target. Nevertheless, most existing methods face challenges in obstacle avoidance, as it might be required to find an extremely specific and not direct result to surpass a certain obstacle, causing local maxima to be found. This paper proposes the use of a known path-finding method such as the A* Search algorithm establish an optimized route for the arm to extend over, before the evolutionary algorithm is applied to determine the configurations of arm joints. Specifically, the process starts with a predefined scenario containing both the target, the starting point and a set of obstacles. The A* algorithm is then used to map a route, which is divided into a set of targets that the robotic arm must follow to reach its final destination. The evaluation function used in this project ensures that proximity to obstacles negatively affects individual fitness. The experimental findings show that this strategy is able to find a way around local maxima quickly and outperform previous purely evolutionary approaches in both 2D and 3D simulated environments.