Collision Avoidance in Unstructured Environments for Autonomous Robots: A Behavioural Modelling Approach

Abstract

Collision avoidance is one of the important safety key operations that needs attention in the navigation system of an autonomous robot. In this paper, a Behavioural Bayesian Network approach is proposed as a collision avoidance strategy for autonomous robots in an unstructured environment with static obstacles. In our approach, an unstructured environment was simulated and the information of the obstacles generated was used to build the Behavioural Bayesian Network Model (BBNM). This model captures uncertainties from the unstructured environment in terms of probabilities, and allows reasoning with the probabilities. This reasoning ability enables autonomous robots to navigate in any unstructured environment with a higher degree of belief that there will be no collision with obstacles. Experimental evaluations of the BBNM show that when the robot navigates in the same unstructured environment where knowledge of the obstacles is captured, there is certainty in the degree of belief that the robot can navigate freely without any collision. When the same model was tested for navigation in a new unstructured environment with uncertainties, the results showed a higher assurance or degrees of belief that the robot will not collide with obstacles. The results of our modelling approach show that Bayesian Networks (BNs) have good potential for guiding the behaviour of robots when avoiding obstacles in any unstructured environment.