Abstract
An algorithm for machine learning of a transport type model is presented for the optimal distribution of tasks in a heterogeneous group of robots operating in an automatic mode without operator participation. It is assumed that the model is trained by an experienced operator in a landfill environment adequate to a real emergency situation in which robots are to perform operations. According to the configured model in a real setting, tasks can be distributed according to a supervisory or decentralized control scheme. Training can be carried out and in the process of the regular operation of robots. In this case, the use of the learning model allows you to split the configuration tuning circuits and the assignment of tasks, which enables the robots and the operator to function at their own natural pace.
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