Predictive Planning for Heterogeneous Human-Robot Teams

Abstract

This paper addresses the problem of task allocation over a heterogeneous team of human operators and robotic agents with the object of improving mission eciency and reducing costs. A distributed systems-level predictive approach is presented which simultaneously plans schedules for the human operators and robotic agents while accounting for agent availability, workload and coordination requirements. The approach is inspired by the Consensus-Based Bundle Algorithm (CBBA), a distributed task allocation framework previously developed by the authors, which is used to perform the task coordination for the team in a dynamic environment. Results show that predictive systems-level planning improves mission performance, distributes workload eciently among agents, reduces operator over-utilization and leads to coordinated agent behavior.