Project Description

In high-speed technologies world, where every operation needs to be performed instantaneously and more efficiently, the scientists and engineers have created a Bio-inspired algorithms to solve the problems encountered in the real-world activities. The Ant Colony Optimisation (ACO) algorithm is one such solution that assists in solving the problems of robot path planning. The ACO algorithms have been developed for different problems in last couple of decades. The common problems experienced by many of these algorithms are large computational time and some are non-dynamic in nature. Due to this reasons, the ACO algorithms are not being used on a large number of applications.

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.