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Mobile robot path planning with modified ant colony optimisation

Automated navigation is a pivotal task of robotics research and the key challenge lies in robot motion on unknown dynamic terrain. The large number of solutions to robotic path planning, especially in unknown and dynamic environments, mainly rely on the heuristic methods. The most important factor for this choice is the fast convergence towards solution without supervision. In the proposed scheme we have used a modified version of ant colony optimisation. We incorporated the directional movement history of robot on a grid into a vector as a probability multiplication factor which helps to achieve a faster convergence and avoid unnecessary movements, e.g., looping. In this work we have devised a novel pheromone updation scheme. Along with this we have applied path smoothing to lessen the number of turns on the candidate optimal path. Effectiveness is shown through several extensive experiments and results clearly indicate the aptness of the proposed scheme.