Search

This study examined the impact of allowing an operator to adjust the rate of prompts to view automation-generated plans on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Future unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple vehicles with different capabilities, connected through a decentralized network. A previous experiment showed that higher rates of replan prompting led to higher workload and lower 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. Method: Three initial rates 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. Operators were provided with the ability to adjust the rate of replanning. Results: The majority of the operators chose to adjust the rate at which they were prompted to replan. Operators favored particular replan intervals, no matter which initial replan interval they started at. It was found that different initial replan intervals produced differences in mission performance. In addition, increasing amounts of replanning caused the system to destroy more targets but do a poorer job at tracking targets. Conclusion: Operators have preferences for the rate at which they prefer to view automation-generated plans. Allowing operators to institute these preferences influenced the overall mission performance. Further research is necessary to determine the full impact of the operators’ strategies for changing the replan intervals on net mission performance. Application: Future unmanned vehicles systems designs should incorporate the flexibility to allow operators to adjust the frequency at which the automation generates new plans for approval.

Advances in autonomy have made it possible to invert the typical operator-to-unmanned vehicle ratio so that a single operator can now control multiple heterogeneous Unmanned Vehicles (UVs). Real-time scheduling and task assignment for multiple UVs in uncertain environments will require the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors through mixed-initiative systems. The goal of this paper is to analyze the interactions between operators and scheduling algorithms in two human- in-the-loop multiple UV control experiments. The impact of real-time operator modifications to the objective function of an optimization algorithm for multi-UV scheduling is described. Results from outdoor multiple UV flight tests using a human-computer collaborative scheduling system are presented, which provide valuable insight into the impact of environmental uncertainty and vehicle failures on system effectiveness.

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.