A Stochastic Queueing Model for Multi-robot Task Allocation

A central problem in multi-robot systems is to solve the multi-robot task allocation problem. In this paper, a decentralized stochastic model based on stochastic queueing processes is applied for an application of collective detection of underground landmines where the robots are not told the distribution or number of landmines to be encountered in the environment. Repeat demands of inspection in the environment to ensure the accuracy of robot findings are necessary in this application. The proposed model is based on the estimation of a stochastic queue of pending demands that represents the alternatives of action for a robot and is used to negotiate possible conflicts with other robots. We compare and contrast this method with a decentralized greedy approach based on the distance towards the sites where inspection demands are required. Experimental results obtained using simulated robots in the Webots c ⃝ environment are presented. The performance of robots is measured in terms of two metrics, completion time and distance traveled for processing a demand. Robots applying the stochastic queueing model obtained competitive results.