A Supervisory-Based Collaborative Obstacle-Guided Path Refinement Algorithm for Path Planning in Wide Terrains

摘要

Robotic exploration of wide terrains, such as agricultural fields, could be challenging while considering the limited robot's capabilities in terms of sensing and power. Thus, in this article, we proposed OGPR*, an Obstacle Guided Path Refinement algorithm for quickly planning collision-free paths utilizing the obstacles existing in the environment. To tackle the issue of exploring wide terrains, a supervisory-based collaboration between the quadcopter and a mobile robot is proposed. The quadcopter is responsible for streaming subsequently live two-dimensional images for the environment under discussion while planning safe paths for the ground the mobile robot is planning safe paths to manoeuvre. Numerical simulations proved the significant performance of the proposed OGBR* algorithm when compared to the state of the art algorithms exist in the literature.