An Integrated Framework for Cooperative Aerial Manipulators in Unknown Environments

Lee, Hyeonbeom; Kim, Hyoin; Kim, Woo Jin; Kim, H. Jin

doi:10.1109/lra.2018.2807486

Cited by 36 publications

(39 citation statements)

References 23 publications

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“…Centralized solutions have been proposed in Fink et al (2011), where cooperative transportation is addressed by means of cables, and in Gancet et al (2005) and Brandao et al (2014). Another efficient paradigm for transportation with multiple aerial vehicles is the master-slave approach (Tagliabue et al 2017;Lee et al 2018), where the master moves the payload in the desired direction while the slave actively guarantees compliance to the master's motion.…”

Section: Overviewmentioning

confidence: 99%

“…The cooperative transportation problem has been addressed also in Yang and Lee (2015), where user-specified desired behavior, including, e.g., trajectory tracking and compliant interaction, is endowed by an optimal cooperative force distribution and by designing an individual quadrotor-manipulator control based on object stiffness model. Both Caccavale et al (2015) and Yang and Lee (2015) propose realistic simulations for validating their algorithms, while experiments in the presence of a grasped rigid object in an obstacle environment are reported in Lee et al (2018), where the trajectory in joint space of each aerial manipulator is generated by using a task priority solution in order to satisfy unilateral constraints such as handling the interaction forces and torques.…”

Section: Third Layer: Low-level Controlmentioning

confidence: 99%

“…A widely investigated application is the cooperative transportation of an object by multiple aerial robots via cables (Fink et al 2011;Tagliabue et al 2017;Michael et al 2011): the use of cables confers a certain compliance to the system, although payload stabilization issues are to be properly tackled. The use of grippers or multi-DOFs robotic arms allows to better control the payload pose: in Mellinger et al (2013), a control approach for multiple quadrotor robots, which cooperatively grasp and transport a payload by means of simple grippers, is developed and experimentally tested, while in Lee et al (2018), two UAMs, composed by a cameraequipped aerial vehicle and a two-DOFs robotic arm, cooperatively move a rigid bar of unknown mass via master-slave paradigm.…”

Section: Examples Of Applicationmentioning

confidence: 99%

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Multiple Unmanned Aerial Manipulator Systems, Coordinated Control of

Caccavale¹,

Pierri²

2020

Encyclopedia of Robotics

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Multiple unmanned aerial manipulator systems (UAMS) are teams composed by aerial vehicles equipped by robotic manipulators to execute complex manipulation tasks or transportation of heavy/large payloads. The coordinated control of such systems is aimed at achieving proper motion coordination among the aerial manipulators in the team, so as to guarantee the execution of a given task, defined at the team level.

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Section: Overviewmentioning

confidence: 99%

Section: Third Layer: Low-level Controlmentioning

confidence: 99%

Section: Examples Of Applicationmentioning

confidence: 99%

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Multiple Unmanned Aerial Manipulator Systems, Coordinated Control of

Caccavale¹,

Pierri²

2020

Encyclopedia of Robotics

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“…The centralized top layer plans desired trajectories for each UAM’s end-effector; the NSB method is applied to generate UAM’s motion references for the bottom dynamic controller. Similar to the work [13], the task priority was also employed in [22,23] to generate trajectories for cooperative transportation using multiple aerial manipulators. The dynamic movement primitives (DMPs) were utilized to realize obstacle avoidance in unknown environments.…”

Section: Related Workmentioning

confidence: 99%

Aerial Grasping with a Lightweight Manipulator Based on Multi-Objective Optimization and Visual Compensation

Chen

Quan

Fang

2019

Sensors

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Autonomous grasping with an aerial manipulator in the applications of aerial transportation and manipulation is still a challenging problem because of the complex kinematics/dynamics and motion constraints of the coupled rotors-manipulator system. The paper develops a novel aerial manipulation system with a lightweight manipulator, an X8 coaxial octocopter and onboard visual tracking system. To implement autonomous grasping control, we develop a novel and efficient approach that includes trajectory planning, visual trajectory tracking and kinematic compensation. Trajectory planning for aerial grasping control is formulated as a multi-objective optimization problem, while motion constraints and collision avoidance are considered in the optimization. A genetic method is applied to obtain the optimal solution. A kinematic compensation-based visual trajectory tracking is introduced to address the coupled affection between the manipulator and octocopter, with the advantage of discarding the complex dynamic parameter calibration. Finally, several experiments are performed to verify the effectiveness of the proposed approach.

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“…Unmanned Aerial Manipulator (UAM), which is a type of Unmanned Aerial Vehicles (UAV) equipped with a multiple Degrees-of-Freedom (DoF) robotic arm, is bio-inspired from flying birds and attracts increasing attention in robotics research. UAMs have immense potential for various applications, including express transportation [1][2][3] , construction and maintenance [4][5][6] , manipulation [7][8][9] , and cooperative operations [10][11][12] in places that are dangerous and difficult to reach by humans or ground mobile robots. Grasping is an important application for mobile manipulators.…”

Section: Introductionmentioning

confidence: 99%

Natural Feature-based Visual Servoing for Grasping Target with an Aerial Manipulator

et al. 2020

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Aerial transportation and manipulation have attracted increasing attention in the unmanned aerial vehicle field, and visual servoing methodology is widely used to achieve the autonomous aerial grasping of a target object. However, the existing marker-based solutions pose a challenge to the practical application of target grasping owing to the difficulty in attaching markers on targets. To address this problem, this study proposes a novel image-based visual servoing controller based on natural features instead of artificial markers. The natural features are extracted from the target images and further processed to provide servoing feature points. A six degree-of-freedom (6-DoF) aerial manipulator system is proposed with differential kinematics deduced to achieve aerial grasping. Furthermore, a controller is designed when the target object is outside a manipulator's workspace by utilizing both the degrees-of-freedom of unmanned aerial vehicle and manipulator joints. Thereafter, a weight matrix is used as basis to develop a multi-tasking visual servoing framework to integrate the controllers inside and outside the manipulator's workspace. Lastly, experimental results are provided to verify the effectiveness of the proposed approach.

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An Integrated Framework for Cooperative Aerial Manipulators in Unknown Environments

Cited by 36 publications

References 23 publications

Multiple Unmanned Aerial Manipulator Systems, Coordinated Control of

Multiple Unmanned Aerial Manipulator Systems, Coordinated Control of

Aerial Grasping with a Lightweight Manipulator Based on Multi-Objective Optimization and Visual Compensation

Natural Feature-based Visual Servoing for Grasping Target with an Aerial Manipulator

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