A homogeneous mobile robot team that is fault-tolerant
This paper introduces a design methodology of a fault-tolerant autonomous multi-robot system (MRS). An important fundamental topic for this type of system is the design of an on-line autonomous behavior acquisition mechanism that is capable of developing cooperative roles as well as assigning them to a robot appropriately in a noisy embedded environment. Our approach is to apply reinforcement learning that adopts the Bayesian discrimination method for segmenting a continuous state space and a continuous action space simultaneously. In addition, a neural network is provided for predicting the average of the other robots postures at the next time step in order to stabilize the reinforcement-learning environment. Computer simulations are conducted to illustrate the fault-tolerance of our MRS against a system change that occurs after the MRS achieves stable behavior.
fault-tolerance multi-robot system reinforcement learning autonomous specialization