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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. Algorithms used in unmanned-vehicle path planning and task allocation typically have an objective function that only takes into account variables initially identified by designers with set weightings. This can make the algorithm seemingly opaque to an operator and brittle under changing mission priorities. To address these issues, it is proposed that allowing operators to dynamically modify objective function weightings of an automated planner during a mission can have performance benefits. A multiple-unmanned-vehicle simulation test bed was modified so that operators could either choose one variable or choose any combination of equally weighted variables for the automated planner to use in evaluating mission plans. Results from a human-participant experiment showed that operators rated their performance and confidence highest when using the dynamic objective function with multiple objectives. Allowing operators to adjust multiple objectives resulted in enhanced situational awareness, increased spare mental capacity, fewer interventions to modify the objective function, and no significant differences in mission performance. Adding this form of flexibility and transparency to automation in future unmanned vehicle systems could improve performance, engender operator trust, and reduce errors.

The development of Unmanned Vehicles (UVs) with increasing autonomy has enabled a transition from teleoperation to Human Supervisory Control (HSC). In this demonstration, participants can test three innovative operator interfaces for HSC of UVs. The first system allows users to control a Micro Air Vehicle (MAV) via a hand-held device, such as an iPhone®, through high-level waypoint commands and fine-grained nudge controls. The second system enables a single operator to collaborate with an automated planner to control multiple heterogeneous UVs from a laptop-sized display for searching for, tracking and engaging moving ground targets. The third system is designed to aid in planning on naval aircraft carrier decks, serving as a decision support tool for a supervisor overseeing and scheduling the activity of people, vehicles and unmanned vehicles working in this complex and uncertain environment.

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.