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This paper presents the outdoor ight test results of a decentralized multi-UAV system supervised by a human operator. The system balances the roles of the human operator and the UAV autonomous behaviors with the objective of maximizing the execution performance. The operator manages the mission by inputting and modifying tasks instead of controlling individual UAVs. The Consensus-Based Bundle Algorithm (CBBA) is used as a real-time, scalable, dynamic multi-agent multi-task planning algorithm to allocate tasks approved by the operator to UAVs. A team of three quadrotors and one xed wing UAV collaborated in an operationally relevant scenario supporting a cargo UAV resupply mission. Thirteen of fourteen multi-UAV outdoor ight test trials successfully accomplished the mission objectives. The framework was shown to be robust to system failures and degradations commonly encountered during eld testing primarily because of health monitoring and management tools that were incorporated in the design. Instances of task allocation and path planning churning were observed which are linked to uncertainties of operating outdoors. Lessons learned during ight test operations are highlighted as they are relevant to other similar types of systems and missions.

Real-time scheduling and task assignment for multiple Unmanned Vehicles (UVs) in uncertain environments will require the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors. Identifying the characteristics that make a scheduling algorithm suitable for human-computer collaboration is essential for the development of an effective scheduling system. This high-level systems analysis paper begins the process of deriving requirements for collaborative scheduling algorithms by conducting a survey of 117 publications within the past five years in academia and industry on multiple UV scheduling algorithms. The goal of the survey is to determine the types and frequency of scheduling algorithms that are currently in use and to classify the characteristics and capabilities of these algorithms. Results show that academia has settled on meta-heuristic and auction-based algorithms as providing the best balance of performance and computational speed. In industry, however, the most widely used solution methods are “iterative” approaches that monotonically improve the schedule with further iterations. Industry-developed algorithms are more likely to be capable of scheduling heterogeneous UVs, but university researchers have developed more algorithms that can account for uncertainty and provide estimates of robustness. The different objectives of industry practitioners and academic researchers may be driving these disparities. Addressing this gap will be essential to the development and adoption of future humancomputer collaborative scheduling systems.

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.