The flying hand: A formation of UAVs for cooperative aerial tele-manipulation

Gioioso, Guido; Franchi, Antonio; Salvietti, Gionata; Scheggi, Stefano; Prattichizzo, Domenico

doi:10.1109/icra.2014.6907490

Cited by 86 publications

(61 citation statements)

References 22 publications

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“…In [39] it is proposed a control framework to let a group of aerial robots grasp an object in a way that each robot uses an attached rigid tool to establish a single contact, see Figure 11. Each robot acts as a flying finger that collaborates with the others to establish a N-fingered hand.…”

Section: E Physical Interaction With Contactmentioning

confidence: 99%

Human-Collaborative Schemes in the Motion Control of Single and Multiple Mobile Robots

Franchi

2017

Trends in Control and Decision-Making for Human–Robot Collaboration Systems

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Abstract-In this chapter we show and compare several representative examples of human-collaborative schemes in the control of mobile robots, with a particular emphasis on the aerial robot case. We first provide a simplified yet descriptive model of the robot and its interactions. We then use this model to define a taxonomy that highlights the main aspects of these collaboration schemes, such as: the physical domain of the robots, the degree of autonomy, the force interaction with the operator (e.g., the unilateral versus the bilateral haptic shared control), the near-operation versus the teleoperation, the contact-free versus the physically interactive situation, the use of onboard sensors, and the presence of a time-horizon in the operator reference. We then specialize the proposed taxonomy to the multi-robot case in which we further distinguish the methods depending on their level of centralization, the presence of leader-follower schemes, of formation control schemes, the ability to preserve graph theoretical properties, and to perform cooperative physical interaction. The common denominator of all the examples presented in this chapter is the presence of a human operator in the control loop. The main goal of the chapter is to introduce the reader and provide a firstlevel analysis on the several ways to effectively include human operators in the control of both single and multiple aerial robots and, by extension, of more generic mobile robots.

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Section: E Physical Interaction With Contactmentioning

confidence: 99%

Human-Collaborative Schemes in the Motion Control of Single and Multiple Mobile Robots

Franchi

2017

Trends in Control and Decision-Making for Human–Robot Collaboration Systems

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“…Since observability depends on the angular velocity ω = x 3 , it is important to analyze the trajectories of system (21). In particular, trajectories for which the angular velocity is constantly zero or asymptotically converges to zero would lead to an unobservable system.…”

Section: A Estimation Of the Moment Of Inertia J It Is Easy To Verifmentioning

confidence: 99%

“…Note that we do not assume that the robot can measure the position of the contact point, nor its acceleration. 3 Furthermore, we do not assume kinematically controlled robots, i.e., robots cannot set the speed of the contact point at will.…”

Section: Problem Statementmentioning

confidence: 99%

“…In [2], the control of robotic teams with the aim of combining optimal goal regulation and relaxation of the formation rigidity constraint is carried out. Cooperative manipulation has also been dealt with in aerial applications [3], [4], using cables, or other interaction tools. However, the majority of the works does not focus on an accurate dynamic modeling of the payload, which is either modeled as a point mass or as a rigid body with unknown dynamical parameters.…”

Section: Introductionmentioning

confidence: 99%

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Distributed estimation of the inertial parameters of an unknown load via multi-robot manipulation

Franchi

Petitti

Rizzo

2014

53rd IEEE Conference on Decision and Control

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Abstract-In this paper, we propose a distributed strategy for the estimation of the kinematic and inertial parameters of an unknown body manipulated by a team of mobile robots. We assume that each robot can measure its own velocity, as well as the contact forces exerted during the body manipulation, but neither the accelerations nor the positions of the contact points are directly accessible. Through kinematics and dynamics arguments, the relative positions of the contact points are estimated in a distributed fashion, and an observability condition is defined. Then, the inertial parameters (i.e., mass, relative position of the center of mass and moment of inertia) are estimated using distributed estimation filters and a nonlinear observer in cooperation with suitable control actions that ensure the observability of the parameters. Finally, we provide numerical simulations that corroborate our theoretical analysis.

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“…Groups of robots have a high potential to achieve common tasks which are too complex for a single robot, with promising applications for exploration [1], [2], formation control [3], [4], transportation [5]- [7], assembly/construction [8], [9] and many more. The coordination of robots to achieve a task offers more flexibility, robustness and scalability because tasks can be reallocated between the robots if the resources are not sufficient.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot path planning with maintenance of generalized connectivity

Solana

Furci

Cortés

et al. 2017

2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)

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Abstract-This paper addresses the problem of generating a path for a fleet of robots navigating in a cluttered environment, while maintaining the so called generalized connectivity. The main challenge in the management of a group of robots is to ensure the coordination between them, taking into account limitations in communication range and sensors, possible obstacles, inter-robot avoidance and other constraints. The Generalized Connectivity Maintenance (GCM) theory already provides a way to represent and consider the aforementioned constraints, but previous works only find solutions via locally-steering functions that do not provide global and optimal solutions. In this work, we merge the GCM theory with randomized pathplanning approaches, and local path optimization techniques to derive a tool that can provide global, good-quality paths. The proposed approach has been intensively tested and verified by mean of numerical simulations.

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The flying hand: A formation of UAVs for cooperative aerial tele-manipulation

Cited by 86 publications

References 22 publications

Human-Collaborative Schemes in the Motion Control of Single and Multiple Mobile Robots

Human-Collaborative Schemes in the Motion Control of Single and Multiple Mobile Robots

Distributed estimation of the inertial parameters of an unknown load via multi-robot manipulation

Multi-robot path planning with maintenance of generalized connectivity

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