A Hierarchical Coordination Framework for Joint Perception-Action Tasks in Composite Robot Teams

Abstract

We propose a collaborative planning and control algorithm to enhance cooperation for composite teams of autonomous robots in dynamic environments. Composite robot teams are groups of agents that perform different tasks according to their respective capabilities in order to accomplish an overarching mission. Examples of such teams include groups of perception agents (can only sense) and action agents (can only manipulate) working together to perform disaster response tasks. Coordinating robots in a composite team is a challenging problem due to the heterogeneity in the robots’ characteristics and their tasks. Here, we propose a coordination framework for composite robot teams. The proposed framework consists of two hierarchical modules: First, a multiagent state-action-reward-time-state-action algorithm in multiagent partially observable semi-Markov decision process as the high-level decision-making module to enable perception agents to learn to surveil in an environment with an unknown number of dynamic targets and second, a low-level coordinated control and planning module that ensures probabilistically guaranteed support for action agents. Simulation and physical robot implementations of our algorithms on a multiagent robot testbed demonstrated the efficacy and feasibility of our coordination framework by reducing the overall operation times in a benchmark wildfire-fighting case study.