Gesture based human - Multi-robot swarm interaction and its application to an interactive display

Alonso–Mora, Javier; Lohaus, S. Haegeli; Leemann, Philipp; Siegwart, Roland; Beardsley, Paul

doi:10.1109/icra.2015.7140033

Cited by 53 publications

(30 citation statements)

References 15 publications

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“…In [11], a haptic interface is used to control the formation of a group of quadrotors. Gesture based human-swarm interaction is seen in [12], in which authors deciphered human gestures to drive multiple ground mobile robots to show different faces. Obstacle avoidance for single and multiple quadrotors is also heavily researched in the literature.…”

Section: Introductionmentioning

confidence: 99%

Assistive collision avoidance for quadrotor swarm teleoperation

Zhou

Schwager

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents a method for controlling quadrotor swarms in an environment with obstacles, with an intuitive human-swarm interface operated by a single human user. Our method allows for the quadrotor swarm to maintain a desired formation, while also keeping the quadrotors a safe distance from obstacles and from one another. We use a Virtual Rigid Body abstraction to provide a bridge between the single human user and the quadrotor swarm, so that a human user can fly an arbitrarily large quadrotor swarm from a single joystick. By applying multiple vector fields, collisions are automatically avoided within the swarm of quadrotors, and between the quadrotors and obstacles, while the Virtual Rigid Body is controlled by the human user. Our method is demonstrated in hardware experiments with groups of quadrotor micro aerial vehicles teleoperated by a single human operator in a motion capture system.

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Section: Introductionmentioning

confidence: 99%

Assistive collision avoidance for quadrotor swarm teleoperation

Zhou

Schwager

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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“…For example, [2,27] consider the scenario of formation control. It is obvious, that a single human is not able to simulateously control the positions of an even moderately high number of robots in the team in order to keep…”

Section: Command Interfacesmentioning

confidence: 99%

“…In the active role, the human provides continuous commands, for example motion and/or force commands. This type of commands is typically conveyed through a motion measuring device such as a touch screen [54], vision-based system [102], [2], or a haptic device [64,81].…”

Section: Command Interfacesmentioning

confidence: 99%

Control sharing in human-robot team interaction

Musić

Hirche

2017

Annual Reviews in Control

122

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The interaction between humans and robot teams is highly relevant in many application domains, for example in collaborative manufacturing, search and rescue, and logistics. It is well-known that humans and robots have complementary capabilities: Humans are excellent in reasoning and planning in unstructured environments, while robots are very good in performing tasks repetitively and precisely. In consequence, one of the key research questions is how to combine human and robot team decision making and task execution capabilities in order to exploit their complementary skills. From a controls perspective this question boils down to how control should be shared among them. This article surveys advances in humanrobot team interaction with special attention devoted to control sharing methodologies. Additionally, aspects affecting the control sharing design, such as human behavior modeling, level of autonomy and humanmachine interfaces are identified. Open problems and future research directions towards joint decision making and task execution in human-robot teams are discussed.

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“…The operator can then modify the positions of the robots through the tablet. A similar interaction is studied in [1] where body gestures are used as command inputs through a Kinect to organize and position the swarm. The main difference of our system is that the human needs to interact with only a single robot, and yet gains control over the cooperative actions of the entire swarm.…”

Section: Introductionmentioning

confidence: 99%

Using Google Glass in Human–Robot Swarm Interaction

Kapellmann-Zafra

Chen

Groß

2016

Towards Autonomous Robotic Systems

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Abstract. We study how a human operator can guide a swarm of robots when transporting a large object through an environment with obstacles. The operator controls a leader robot that influences the other robots of the swarm. Follower robots push the object only if they have no line of sight of the leader. The leader represents a way point that the object should reach. By changing its position over time, the operator effectively guides the transporting robots towards the final destination. The operator uses the Google Glass device to interact with the swarm. Communication can be achieved via either touch or voice commands and the support of a graphical user interface. Experimental results with 20 physical e-puck robots show that the human-robot interaction allows the swarm to transport the object through a complex environment.

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Gesture based human - Multi-robot swarm interaction and its application to an interactive display

Cited by 53 publications

References 15 publications

Assistive collision avoidance for quadrotor swarm teleoperation

Assistive collision avoidance for quadrotor swarm teleoperation

Control sharing in human-robot team interaction

Using Google Glass in Human–Robot Swarm Interaction

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