In this project, a new ACO algorithm is proposed which ensures to solve the problems encountered in traditional ACO algorithms. This algorithm was tested on number of environments to examine efficiency, error margin and count computational time. The results ensured that the proposed ACO algorithm is completely efficient in small-scale environments and remarkably similar results were observed on testing it on the bigger-scale environment. The evaluations prove that the Ant Colony Optimization algorithm for path planning can provide rapid path planning with acceptable results and for future development can be integrated with the robot system to test in the real world scenarios.
