Arithmetic mean-based decision-making algorithm for obstacle avoidance and multiple intruder detection - Arif, M. U.; Marzoughi, A. (2020 Australian and New Zealand Control Conference (ANZCC), 2020)
This paper demonstrates a decentralized strategy of an arithmetic mean-based navigation algorithm for a group of mobile robots. The aim is to have them navigate through an unknown environment surrounded by obstacles to detect and follow multiple invading intruders. The suggested navigation strategy ensures that the mobile robots safely manoeuvre around surrounding obstacles and avoid collision. Furthermore, a probability of danger algorithm is introduced to ensure that all the intruders present in the environment are followed by at least one robot when they are observed at any time t *. The mobile robots follow the intruders' movements on the basis of their pixel values using a Microsoft Kinect sensor. A low pixel value means that the intruder is far away, and a high pixel value represents the close proximity of the intruder to the mobile robots. All the algorithms and image processing techniques are implemented and tested in a Webots simulation environment using C programming language. The results show the success of the proposed arithmetic mean-based navigation and probability of danger-based intruder-following algorithm.