Development of cooperative behavioural model for autonomous multi-robots system deployed to underground mines

Abstract

The number of disasters that occur in underground mine environments monthly all over the world cannot be ignored. Some of these disasters for instance are roof-falls; explosions, toxic gas inhalation, in-mine vehicle accidents, etc. can cause fatalities and/or disabilities. However, when such accidents happen during mining operations, rescuers find it difficult to respond to it immediately. This creates the necessity to bridge the gap between the lives of miners and the product acquired from the underground mines by using multi-robot systems. This thesis proposes an autonomous multi-robot cooperative behavioural model that can help to guide multi-robots in pre-entry safety inspection of underground mines. A hybrid swarm intelligent model termed, QLACS, that is based on Q-Learning (QL) and the Ant Colony System (ACS) is proposed to achieve cooperative behaviour in a MRS. The intelligent model was developed by harnessing the strengths of both QL and ACS algorithms. The ACS is used to optimize the routes used for each robot while the QL algorithm is used to enhance cooperation among the autonomous robots. The communication within the QLACS model for cooperative behavioural purposes is varied. The performance of the algorithms in terms of communication was evaluated by using a simulation approach. An investigation is conducted on the evaluation/scalability of the model using the different numbers of robots. Simulation results show that the methods proposed in this thesis achieved cooperative behaviour among the robots better than state-of-the-art or other common approaches. Using time and memory consumption as performance metrics, the results reveal that the proposed model can guide two, three and up to four robots to achieve efficient cooperative inspection behaviour in underground terrains.