We address the problem of the interaction (see below) between humans and groups of robots whose local synergy is exploited to accomplish complex tasks. Multi-robot systems possess several advantages w.r.t. single robots, e.g., higher performance in simultaneous spatial domain coverage, better affordability as compared to a single/bulky system, robustness against single point failures. As multi-robot platform, we considered the case of a group of Unmanned Aerial Vehicles (UAVs), because of their high motion flexibility and potential pervasivity in dangerous or unaccessible locations. We envision a scenario where the UAVs possess some level of local autonomy and act as a group, e.g., by maintaining a desired formation, by avoiding obstacles, and by performing additional local tasks. At the same time, the remote human operator is in control of the overall UAV motion and receives, through haptic feedback, suitable cues informative enough of the remote UAV/environment state. We addressed two distinct possibilities for the human/multi-robot teleoperation: a top-down approach, and a bottom-up approach, mainly differing in the way the local robot interactions and desired formation shape are treated.
In  the N UAVs are abstracted as 3-DOF first-order kinematic VPs (virtual points): the remote human user teleoperates a subset of these N VPs, while the real UAV's position tracks the trajectory of its own VP. The VPs collectively move as an N-nodes deformable flying object, whose shape (chosen beforehand) autonomously deforms, rotates and translates reacting to the presence of obstacles (to avoid them), and the operator commands (to follow them). The operator receives a haptic feedback informing him about the motion state of the real UAVs, and about the presence of obstacles via their collective effects on the VPs. Passivity theory is exploited to prove stability of the overall teleoperation system. A video showing the top-down approach is here.
In , the approach has been extended to the case where bearing measurements are the only available. The main focus in that case have been the design of a novel decentralized and minimum-complexity bearing-only formation controller for the slave side.
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The figure shows an example of 8 UAVs arranged in a cubic shape controlled with an haptic device.
In  the N UAVs are abstracted as 3-DOF second-order VPs: the remote human user teleoperates a single leader, while the remaining followers motion is determined by local interactions (modeled as spring/damper couplings) among themselves and the leader, and repulsive interactions with the obstacles. The overall formation shape is not chosen beforehand but is a result of the UAVs motion. Split and rejoin decisions are allowed depending on any criterion, e.g., the UAVs relative distance and their relative visibility (i.e., when two UAVs are not close enough or obstructed by an obstacle, they split their visco-elastic coupling). The operator receives a haptic feedback informing him about the motion state of the leader which is also influenced by the motion of its followers and their interaction with the obstacles. Passivity theory is exploited to prove stability of the overall teleoperation system. A journal paper about the bottom-up approach has been recently submitted and it is currently under revision .
In  we present a decentralized passivity-based control strategy for the bilateral teleoperation of a fleet of Unmanned Aerial Vehicles (UAVs). The human operator at the master side can command the fleet motion and receive suitable force cues informative about the remote environment. By properly controlling the energy exchanged within the slave side (the UAV fleet), we guarantee that the connectivity of the fleet is preserved and we prevent inter-agent and obstacle collisions. At the same time, we allow the behavior of the UAVs to be as flexible as possible with arbitrary split and join maneuvers. The results of the paper are validated through semi-experiments.
An experimental validation of the bottom-up approach has been presented in , while in  an extension of the approach where the group autonomously changes the leader in order to maximize the performances is presented.
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The figure shows an example of 6 UAVs among 4 obstacles and relative connectivity graph. The leader UAV is surrounded by a transparent red sphere.
Top-down approach video
Bottom-up approach video