Centroid-based cell decomposition robot path planning algorithm integrated with a bio-inspired approach

Abstract

Bio-inspired algorithms have been increasingly applied for autonomous robot path planning problems in complex environments. These environments are often restrictive in nature, where robot navigation must succeed with low margins of error. The complexity of the environment is a performance limiting factor based on density of obstacles and navigability of the robot in difficult environments. The scale of the environment to be examined for any given problem also contributes to the performance of solutions for path planning. These performance limitations are especially evident in time sensitive real-world applications, like autonomous off-road vehicles or search and rescue situations, where computation quality and immediacy are highly valued. One method to mitigate the shortcomings of bio-inspired algorithms involves destructing the problem environment into readily solvable segments. This paper proposes a graph-based near optimal path approach leveraging a bio-inspired algorithm for rapid path planning in autonomous environments. The proposed model utilizes centroid cell decomposition to establish a map in complex environments in a graph-based form. In this approach, centroid points are regulated and determined by the bio-inspired optimization as part of generating final robot trajectories. To improve upon the shortcomings of typical graph-based algorithms, ant colony optimization is applied afterwards to determine the near optimal robot traversal path. The model is validated through simulated environments for performance with comparable algorithms.