A Comparison of Genetic and Ant Colony Optimization Methods for Robot Motion Routing

This paper investigates the routing problem of motion robot. Routing was considered as offline, which means that the robot has complete information about environmental obstacles. Also, the motion environment was considered square and flat. Then it simulates the efficiency of two routing methods based on genetic and ant colony algorithms in MATLAB software environment. This research uses four experimental environments based on different complexities that their indexes are number and distribution of obstacles. It is expected that robot moves from source route to target route in static environment without contact with obstacles in minimum time and shortest route. We studied the efficiency of two algorithms for robot in moving time from source cell to target cell (in second), moved distance by robot from source point to target point(in meter) and arriving time to first answer(in second). The simulation results of two routing algorithms in the four mentioned environments are as follows:1) Routing robot based on genetic algorithm performed better than routing based on ant colony algorithm in moving time from source cell to target cell.2) Routing robot based on genetic algorithm performed better than routing based on ant colony algorithm in moving distance. 3) Both of routing method performed almost similar performance in finding the first answerexcept in one case.