Landing of a Quadrotor on a Moving Target Using Dynamic Image-Based Visual Servo Control

Serra, Pedro; Cunha, Rita; Hamel, Tarek; Cabecinhas, David; Silvestre, Carlos

doi:10.1109/tro.2016.2604495

Cited by 143 publications

(80 citation statements)

References 26 publications

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“…D. Lee, Ryan, and Kim (2012) demonstrated the viability of the task using visual servoing to maneuver above the moving pattern, and relying on a motion-capture system for external state estimation. A similar system was demonstrated by Serra, Cunha, Hamel, Cabecinhas, and Silvestre (2016) who also use visual servoing but do not rely on a vision-based distance estimation to the target. In comparison to our system, both approaches are evaluated with a slow or even static target.…”

Section: Related Workmentioning

confidence: 98%

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

Beul

Nieuwenhuisen

Quenzel

et al. 2018

Journal of Field Robotics

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The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.

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Section: Related Workmentioning

confidence: 98%

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

Beul

Nieuwenhuisen

Quenzel

et al. 2018

Journal of Field Robotics

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“…The information was used to precisely estimate the UAV pose. Similarly, [24,25] adopted image-based visual servo control to track and land on a moving platform.…”

Section: Related Workmentioning

confidence: 99%

Sim-to-Real Quadrotor Landing via Sequential Deep Q-Networks and Domain Randomization

et al. 2020

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The autonomous landing of an Unmanned Aerial Vehicle (UAV) on a marker is one of the most challenging problems in robotics. Many solutions have been proposed, with the best results achieved via customized geometric features and external sensors. This paper discusses for the first time the use of deep reinforcement learning as an end-to-end learning paradigm to find a policy for UAVs autonomous landing. Our method is based on a divide-and-conquer paradigm that splits a task into sequential sub-tasks, each one assigned to a Deep Q-Network (DQN), hence the name Sequential Deep Q-Network (SDQN). Each DQN in an SDQN is activated by an internal trigger, and it represents a component of a high-level control policy, which can navigate the UAV towards the marker. Different technical solutions have been implemented, for example combining vanilla and double DQNs, and the introduction of a partitioned buffer replay to address the problem of sample efficiency. One of the main contributions of this work consists in showing how an SDQN trained in a simulator via domain randomization, can effectively generalize to real-world scenarios of increasing complexity. The performance of SDQNs is comparable with a state-of-the-art algorithm and human pilots while being quantitatively better in noisy conditions.

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“…Visual servoing is a valid option to some extent [13], [14]; nevertheless, it requires the landing platform to be visible throughout the entire duration of the task, the reason being that the UAV is pulled towards the goal using solely visual information from its camera. To deal with missing visual information, model-based approaches have been proposed to predict the motion of the landing target [15], [16].…”

Section: A Related Workmentioning

confidence: 99%

Vision-based autonomous quadrotor landing on a moving platform

Falanga

Zanchettin

Simovic

et al. 2017

2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR)

144

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Abstract-We present a quadrotor system capable of autonomously landing on a moving platform using only onboard sensing and computing. We rely on state-of-the-art computer vision algorithms, multi-sensor fusion for localization of the robot, detection and motion estimation of the moving platform, and path planning for fully autonomous navigation. Our system does not require any external infrastructure, such as motioncapture systems. No prior information about the location of the moving landing target is needed. We validate our system in both synthetic and real-world experiments using low-cost and lightweight consumer hardware. To the best of our knowledge, this is the first demonstration of a fully autonomous quadrotor system capable of landing on a moving target, using only onboard sensing and computing, without relying on any external infrastructure. SUPPLEMENTARY MATERIALVideo of the experiments: https://youtu.be/ Tz5ubwoAfNE

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Landing of a Quadrotor on a Moving Target Using Dynamic Image-Based Visual Servo Control

Cited by 143 publications

References 26 publications

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

Sim-to-Real Quadrotor Landing via Sequential Deep Q-Networks and Domain Randomization

Vision-based autonomous quadrotor landing on a moving platform

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