Mobile robot path planning with obstacle avoidance using chemical reaction optimization

The usability of robots is expanding day by day as it is capable of doing complex and hazardous tasks better and faster than human beings. To increase the capacity of robot modernization of its motion is inexorable, and finding the obstacle-free and shortest path for the robot in minimum time becomes essential. To solve this mobile robot path planning problem, many exact, heuristic and metaheuristic algorithms were designed and developed. Here, a metaheuristic algorithm based on chemical reaction optimization (CRO) is proposed to find the obstacle-free minimum path in minimum computational time. To get this outcome, basic operators of CRO are redesigned and two new repair operators have been introduced. These repair operators help to reduce the path length, increase the path smoothness and minimize the number of points in a path, respectively,. They have great influence because the four fundamental operators of CRO are not sufficient enough to produce better results in all situations. To prove the supremacy of the proposed algorithm, the results are compared with ant colony optimization algorithm, probabilistic road map method, particle swarm optimization algorithm and genetic algorithm. The comparison shows that the proposed algorithm has the best results in the improvement of path length, smoothness and execution time. The superiority of the proposed algorithm over the compared algorithms has been proven using a statistical test. Besides this, the empirical outcomes of 10 complex maps are revealed in this research work for which nobody did any experiment in our conjecture.