Emergent Robot Differentiation for Distributed Multi-Robot Task Allocation

Abstract

We present a distributed mechanism for automatically allocating tasks to robots in a manner sensitive to each robot's performance level without hand-coding these levels in advance. This mechanism is an important part of improving multi-robot task allocation (MRTA) in systems where communication is restricted or where the complexity of the group dynamics makes it necessary to make allocation decisions locally. The general mechanism is demonstrated as an improvement on our previously published task allocation through vacancy chains (TAVC) algorithm for distributed MRTA. The TAVC algorithm uses individual reinforcement learning of task utilities and relies on the specializing abilities of the members of the group to produce dedicated optimal allocations. Through experiments with realistic simulator we evaluate the improved algorithm by comparing it to random allocation. We conclude that using softmax action selection functions on task utility values makes algorithms responsive to different performance levels in a group of heterogeneous robots.