Mobile Robot Path Planning using Multi-Objective Adaptive Ant Colony Optimization

Abstract

Path planning for a mobile robot is a vital task for navigating in a complex environment. However, the path planning problem is challenging because of its non-deterministic polynomial-time (NP) character. In this research, the authors have proposed the Multi-objective Adaptive Ant Colony Optimization for path planning of mobile robot in a static object environment on the grid. The heuristic information function in the conventional ant colony optimization is modified according to the A* algorithm, which helps in mitigating the slow convergence of the traditional algorithm. It is because the ants will choose the nodes which are closer to the goal position. Additionally, an objective function is formulated as a multiple objective problem by incorporating (a) Path length, (b) Safety factor, and (c) Energy consumption. Simulation results show that the proposed modification helps to achieve the optimal path in a quicker time i.e., 1.3 times faster than the traditional counterpart.