Predictive Planning for Heterogeneous Human-Robot Teams

Abstract

This paper addresses the problem of task allocation over a heterogeneous team of hu- man operators and robotic agents with the object of improving mission efficiency and reducing costs. A distributed systems-level predictive approach is presented which simul- taneously 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 frame- work 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 plan- ning improves mission performance, distributes workload efficiently among agents, reduces operator over-utilization and leads to coordinated agent behavior.