Inference model for heterogeneous robot team configuration based on Reinforcement Learning
Xueqing Sun, Tao Mao, Laura E. Ray · 2009
In many practical robotics problems, knowledge of the team configuration and capabilities is crucial in coordination of multiple heterogeneous robots. In a challenging environment with costly, sporadic, or absent communication, inferencing based on observed spatio-temporal state transitions is necessary for learning and reasoning. In this paper, we present a general purpose inference engine that takes sparse observations of state transitions made during multi-robot team execution of a foraging task as input and dynamically inferences the team configuration through a rational decision-making process using Reinforcement Learning (RL). We demonstrate the operation and scalability of this approach in simulations using various size multi-robot foraging tasks. The method is robust to dynamic changes in team composition during execution.