Robot Path Planning Based on Extended MAKLINK Graph and Improved Ant Colony Optimization

Abstract

In modern robot navigation systems, path planning plays an important role to search the most efficient path throughout the selected environment. In this paper, a novel and effective method is proposed to achieve path planning, combined with extended MAKLINK graph and improved ant colony optimization (IACO). Firstly, the MAKLINK graph is extended to consider not only convex polygonal obstacles but also concave polygonal obstacles. To overcome the disadvantages of traditional ant colony optimization (ACO), an improved ACO is developed by introducing an adaptive updating rule of pheromone. Finally, the simulation results demonstrate that the proposed method is superior to methods based on Dijkstra's algorithm and traditional ACO, planning the shortest path in the complex space environment of concave-convex polygonal obstacles. Compared with traditional ACO, the IACO not only improves the convergence rate and global search capability but also skips the local optimal, proving its effectiveness and feasibility.