Planning and Control for Cooperative Manipulation and Transportation with Aerial Robots

Fink, Jonathan; Michael, Nathan; Kim, Soonkyum; Kumar, Vijay

doi:10.1007/978-3-642-19457-3_38

Cited by 31 publications

(21 citation statements)

References 4 publications

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“…Rotations around r 1 are allowed as they do not break the rigid formation. From (3), if the Leader is at rest so is the virtual Follower; in that case, the formation stays fixed w.r.t. {I}.…”

Section: A Trajectory Planner For a Longitudinal Rigid Linkmentioning

confidence: 99%

“…Multi-robot systems outperform single robot systems in situations where a wide area needs to be covered like terrain mapping or exploration of a sea floor [1], [2]. Another use case is load transportation where a formation of vehicles can handle loads that would be otherwise impossible for a single vehicle to transport [3]. Multiple vehicle formations are also employed in critical missions, where the objective is of paramount importance and robustness to vehicle loss is required [4].…”

Section: Introductionmentioning

confidence: 99%

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Three dimensional trajectory planner for real time leader following

Pereira

Cabecinhas

Cunha

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents an on-line three dimensional trajectory planner for an unmanned vehicle following a Leader vehicle, where the Follower remains at a specified relative position with respect to the Leader. The planner defines the trajectory for a virtual vehicle that will be used as reference for the real Follower vehicle. The virtual Follower behaves like a three dimensional trailer attached to the Leader and its reference frame is used to specify the relative position vector. At the kinematic level, the proposed planner requires only the knowledge of the Leader's linear speed, but not its angular speed, and the Follower's trajectory can be generated on-line, in the sense that no a priori knowledge of the Leader's trajectory is required. Experimental results obtained with quadrotor vehicles are presented, which demonstrate the richness of the planned trajectories.

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Section: A Trajectory Planner For a Longitudinal Rigid Linkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three dimensional trajectory planner for real time leader following

Pereira

Cabecinhas

Cunha

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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show abstract

“…Other interesting but independent works are (where the efforts of Spanish and French researchers are notable) [33], [11], [42], [43], [44], [45], [46], [47], [48], [49], [50]. The second approach is by using multirotor teams manipulating cables or cable-pulley connected effectors Figure 2., the schools of researching are mainly Swiss and American [19], [51], [52], [21], [53], [54], [55], [56], [57], [58], [59]. Also, noteworthy isolated groups [60], [61], [62].…”

Section: Comparisonsmentioning

confidence: 99%

Air-arm: A new kind of flying manipulator

Mendoza-Mendoza

Sepulveda-Cervantes²,

Aguilar-Ibáñez

et al. 2015

2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

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We introduce a new kind of aerial manipulator based on multirotors. This device exploits the rarely used yaw movement which characterizes these aircrafts in order to generate and transmit forces and displacements trough a serial-chain of rigid links and other drones, as if the whole system was a flying robot arm with UAVS as rotational joints. Also we provide comparisons with the existing technologies and a condensed review concerning them. Finally, this paper proposes many open problems in order to develop the final system. Due to the scientific and social impact of this new approach, we provide some application examples.

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“…The formation control [2,4,22] and cooperative manipulation [15,17,21,30] communities have done significant work performing cooperative tasks with a single team of robots. Coltin and Veloso [10] and Kamio and Iba [16] have investigated problems where multiple robots independently deliver packages, exchanging packages only if the cost of delivery will be reduced or if the layout of the workspace necessitates it.…”

Section: Prior Workmentioning

confidence: 99%

Constraint Manifold Subsearch for multirobot path planning with cooperative tasks

Wagner

Kim

Urban³

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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The cooperative path planning problem seeks to determine a path for a group of robots which form temporary teams to perform tasks that require multiple robots. The multi-scale effects of simultaneously coordinating many robots distributed across the workspace while also tightly coordinating robots in cooperative teams increases the difficulty of planning. This paper describes a new approach to cooperative path planning called Constraint Manifold Subsearch (CMS). CMS builds upon M*, a high performance multirobot path planning algorithm, by modifying the search space to restrict teams of robots performing a task to the constraint manifold of the task. CMS can find optimal solutions to the cooperative path planning problem, or near optimal solutions to problems involving large numbers of robots.

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Planning and Control for Cooperative Manipulation and Transportation with Aerial Robots

Cited by 31 publications

References 4 publications

Three dimensional trajectory planner for real time leader following

Three dimensional trajectory planner for real time leader following

Air-arm: A new kind of flying manipulator

Constraint Manifold Subsearch for multirobot path planning with cooperative tasks

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