Highly Flexible Task Planner for Robots in Dynamic Environments

Abstract

Construction site environments are highly non deterministic scenarios under constant changes. Complex tasks are usually required in these scenarios and multi agent systems have been probed as the flexible solution to solve them. Nevertheless, the uncertainty in the environments often makes the available information inaccurate, incomplete or difficult to integrate in multi agent systems. To successfully automate complex processes in construction environments it is necessary to overcome the barrier imposed by the lack of accurate information. The research challenge here presented is the coordination of multi agent systems in non deterministic environments. In this proposal, a Multi Agent Proximal Policy Optimization system (MAPPO) is proposed to create the necessary flexible framework. Various policy networks associated with different types of agents are trained over different scenarios. Different teams of agents are also proposed during the training process. With this approach it is intended to create a framework able to command different teams of agents independently from the constraints imposed by the information of the environment.