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Objective: This study examined the impact of increasing automation replanning rates on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Futuristic unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple dissimilar vehicles connected through a decentralized network. Significant human-automation collaboration will be needed because of automation brittleness, but such collaboration could cause high workload. Method: Three increasing levels of replanning were tested on an existing multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation in conjunction with human supervision. Results: Rapid replanning can cause high operator workload, ultimately resulting in poorer overall system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation’s suggested prompts for new plan consideration as well as negative attitudes toward unmanned aerial vehicles in general. Participants with video game experience tended to collaborate more with the automation, which resulted in better performance. Conclusion: In decentralized unmanned vehicle networks, operators who ignore the automation’s requests for new plan consideration and impose rapid replans both increase their own workload and reduce the ability of the vehicle network to operate at its maximum capacity. Application: These findings have implications for personnel selection and training for futuristic systems involving human collaboration with decentralized algorithms embedded in networks of autonomous systems., Aurora Flight Sciences Corp., United States. Office of Naval Research

A future concept of operations for co ntrolling unmanned vehicles has a single, forwarddeployed soldier supervising multiple, heterogeneous unmanned vehicles. This operator would collaborate with an automated planner that presents the operator with a choice of potential plans for how the unmanned vehicles could efficiently search the dynamic environment for new targets, track identified targets, and destroy hostile targets. A key step towards this future concept is to determine how often the automated planner should provide the operator with potential plans without causing a detrimentally high workload for the operator, which could have negative performance consequences. This research describes the development of the Onboard Planning System for Unmanned Vehicles Supporting Expeditionary Reconnaissance and Surveillance (OPS-USERS) testbed and an experiment to measure operator workload and performance in a set of scenarios with different replanning intervals. The results show that the replan interval has a significant effect on workload and performance with significantly lower performance at the lowest replan interval . Replanning too frequently in a human-on-the-loop collaboration can cause higher levels of workload and lower performance. Further research is required to determine the impact o f low workload in a highly autonomous system on overall performance. Also, operator strategy and trust in automation with the OPS-USERS system deserves further research.