A Net-Launching Mechanism for UAV to Capture Aerial Moving Target

Meng, Xiangying; Ding, Xilun; Pin, Guo

doi:10.1109/icma.2018.8484408

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Other works focus on the catching structure design [18], control [19], visual detection [20] [21], and state estimation [22]. All these have not solved the planning-with-decision problems, so will not be introduced.…”

Section: Related Workmentioning

confidence: 99%

Catch Planner: Catching High-Speed Targets in the Flight

Yu¹,

Wang²,

Wang³

et al. 2023

Preprint

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Catching high-speed targets in the flight is a complex and typical highly dynamic task. In this paper, we propose Catch Planner, a planning-with-decision scheme for catching. For sequential decision making, we propose a policy search method based on deep reinforcement learning. In order to make catching adaptive and flexible, we propose a trajectory optimization method to jointly optimize the highly coupled catching time and terminal state while considering the dynamic feasibility and safety. We also propose a flexible constraint transcription method to catch targets at any reasonable attitude and terminal position bias. The proposed Catch Planner provides a new paradigm for the combination of learning and planning and is integrated on the quadrotor designed by ourselves, which runs at 100hz on the onboard computer. Extensive experiments are carried out in real and simulated scenes to verify the robustness of the proposed method and its expansibility when facing a variety of high-speed flying targets.

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Section: Related Workmentioning

confidence: 99%

Catch Planner: Catching High-Speed Targets in the Flight

Yu¹,

Wang²,

Wang³

et al. 2023

Preprint

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“…However, the solution is limited by vertical grasping, as well as the workspace of the ground manipulator. The most common way to catch flying robots in mid-air is using nets, such as net bullets Meng et al (2018) ; DroneCatcher (2022) , top nets Rodriguez-Ramos et al (2021) ; DroGone (2022) , side nets Vidyadhara et al (2022) , and nets carried by cooperative vehicles Klausen et al (2018) ; Rothe et al (2019) . Despite the proved effectiveness, these solutions primarily focus on catching aerial robots with diagonal sizes (including propellers) over 500 mm (e.g., DJI1 Mavic Pro and Phantom 4).…”

Section: Introductionmentioning

confidence: 99%

Safely catching aerial micro-robots in mid-air using an open-source aerial robot with soft gripper

Liu

Mucchiani

et al. 2022

Front. Robot. AI

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This work focuses on catching safely an aerial micro-robot in mid-air using another aerial robot that is equipped with a universal soft gripper. To avoid aerodynamic disturbances such as downwash, that would push the target robot away, we follow a horizontal grasping approach. To this end, the article introduces a gripper design based on soft actuators that can stay horizontally straight with a single fixture and maintain sufficiently compliance in order to bend when air pressure is applied. Further, we develop the Soft Aerial Gripper (SoAG), an open-source aerial robot equipped with the developed soft end-effector and that features an onboard pneumatic regulation system. Experimental results show that the developed low-cost soft gripper has fast opening and closing responses despite being powered by lightweight air pumps, responses that are comparable to those of a commercially available end-effector tested we test against. Static grasping tests study the soft gripper’s robustness in capturing aerial micro-robots under aerodynamic disturbances. We experimentally demonstrated the feasibility of using the SoAG robot to catch a hovering micro-robot with or without propeller guards. The feasibility of dynamic catching is also shown by capturing a moving aerial micro-robot with a velocity of 0.2 m/s. The free flight performance of the SoAG robot is studied against a conventional quadrotor and in different gripper and payload status.

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“…Counter-UAV systems are one such category. Meng et al [31] presented a manipulation mechanism of launching net for safe target capture. While the downwash and vibration aspects are not serious concerns in the reviewed literature, it is a serious problem for a dynamic target capture task.…”

Section: Introductionmentioning

confidence: 99%

Design and integration of a drone based passive manipulator for capturing flying targets

et al. 2021

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SUMMARY In this paper, we present a novel passive single degree-of-freedom (DoF) manipulator design and its integration on an autonomous drone to capture a moving target. The end-effector is designed to be passive, to disengage the moving target from a flying UAV and capture it efficiently in the presence of disturbances, with minimal energy usage. It is also designed to handle target sway and the effect of downwash. The passive manipulator is integrated with the drone through a single DoF arm, and experiments are carried out in an outdoor environment. The rack-and-pinion mechanism incorporated for this manipulator ensures safety by extending the manipulator beyond the body of the drone to capture the target. The autonomous capturing experiments are conducted using a red ball hanging from a stationary drone and subsequently from a moving drone. The experiments show that the manipulator captures the target with a success rate of 70% even under environmental/measurement uncertainties and errors.

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A Net-Launching Mechanism for UAV to Capture Aerial Moving Target

Cited by 6 publications

References 10 publications

Catch Planner: Catching High-Speed Targets in the Flight

Catch Planner: Catching High-Speed Targets in the Flight

Safely catching aerial micro-robots in mid-air using an open-source aerial robot with soft gripper

Design and integration of a drone based passive manipulator for capturing flying targets

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