Connectivity-Maintenance Teleoperation of a UAV Fleet With Wearable Haptic Feedback

Abstract

This paper presents the design of a decentralized connectivity-maintenance algorithm for the teleoperation of a team of multiple UAVs, together with an extensive human subject evaluation in virtual and real environments. The proposed connectivity-maintenance algorithm enhances earlier works by improving their applicability, safety, effectiveness, and ease of use, by including: (i) an airflow-avoidance behavior that avoids stack downwash phenomena in rotor-based aerial robots; (ii) a consensus-based action for enabling fast displacements with minimal topology changes by having all follower robots moving at the leader's velocity; (iii) an automatic decrease of the minimum degree of connectivity, enabling an intuitive and dynamic expansion/compression of the formation; and (iv) an automatic detection and resolution of deadlock configurations, i.e., when the robot leader cannot move due to counterbalancing connectivityand external-related inputs. We also devised and evaluated different interfaces for teleoperating the team as well as different ways of receiving information about the connectivity force acting on the leader. Results of two human subject experiments show that the proposed algorithm is effective in various situations. Moreover, using haptic feedback to provide information about the team connectivity outperforms providing both no feedback at all and sensory substitution via visual feedback. Note to Practitioners-The control of one drone is usually performed with a remote controller (similar to a joypad) that uses radio-wave signals. When controlling more than one drone, even a simple task such as moving the whole team around become very challenging. Developing an easy, yet efficient way to impart commands to a formation of drones is necessary to achieve any complex task. This work proposes a framework to control a fleet of drones (quadrotors) in an intuitive way, while receiving meaningful and effective information on the state of the formation. The proposed technique does not rely on any absolute positioning system (e.g., GPS) or centralized command center. Instead, it only uses the relative position of the drones with respect to each other, and all computations are designed in a decentralized fashion. These features make the proposed framework ready for deployment in different highimpact applications, such as in surveillance, search-and-rescue, and disaster response scenarios. Index Terms-Multi-robot systems, Human-centered robotics.

Decentralized Control of a Heterogeneous Human–Robot Team for Exploration and Patrolling

“…To the best of our knowledge, there are no examples of complex interactions between multiple humans and robots in this scenario. On the other hand, we can find several examples of human operators teleoperating one or more mobile robots [13], [32], [12], [33], [34], [35], [36]. For example, Setter et al [33] proposed a multi-agent manipulability concept, mapping the forces commanded by the operator through a grounded haptic interface to the motion of a swarm of mobile robots and viceversa.…”

“…The user controls the robot by modifying, at runtime, the trajectory along which the robot is navigating, while an autonomous algorithm ensures that no collisions occur, the path remains smooth, and some points of interest are visited. In [36], we proposed a decentralized connectivity-maintenance algorithm for the teleoperation of a team of multiple UAVs. With respect to this work, [36] considered a pure teleoperation approach, where the human operator is remotely controlling the formation.…”

“…In [36], we proposed a decentralized connectivity-maintenance algorithm for the teleoperation of a team of multiple UAVs. With respect to this work, [36] considered a pure teleoperation approach, where the human operator is remotely controlling the formation. Here, in contrast, we focus on the inclusion of the operator in the team of robots, aiming at achieving increased situational awareness and faster reaction time from the human agent.…”

“…In this work we exploit the connectivity maintenance control strategy originally proposed in [21] and later exploited in several works, e.g., [36]. Indeed, this strategy is general enough for allowing the inclusion of many constraints/requirements of interest into a single generalized connectivity maintenance action, and it also easily allows for the presence of additional external inputs that can be used to steer the individual robots in the group in performing their own mission besides the sole connectivity maintenance constraint.…”

Online Decentralized Perception-Aware Path Planning for Multi-Robot Systems

“…Similarly, a robot pair (i, j) is also assumed able to exchange information over a radio channel when within the sensing range D u . Again, more complex sensing/communication constraints can also be considered, see [30], [31] for some examples. The group interaction is modeled with a directed sensing graph G(V, E), where V = {1, ..., N R } is the vertex set and E ⊆ V × V the edge set.…”