Quadrotor landing on an inclined platform of a moving ground vehicle

Vlantis, Panagiotis; Marantos, Panos; Bechlioulis, Charalampos P.; Kyriakopoulos, Kostas J.

doi:10.1109/icra.2015.7139490

Cited by 67 publications

(48 citation statements)

References 12 publications

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“…They report landing velocities of up to 50 km/h. Landing indoors on an inclined plane was achieved by Vlantis, Marantos, Bechlioulis, and Kyriakopoulos () who designed an adapted model‐predictive controller to optimize the local trajectory in real time. However, due to the computational demand, optimization was done on an external base station and, thus, required a stable network connection.…”

Section: Related Workmentioning

confidence: 99%

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: 99%

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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“…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]. Alternative solutions are realized with the use of additional sensors attached to the moving target.…”

Section: A Related Workmentioning

confidence: 99%

“…This is by no means standard in the literature, since all the approaches mentioned before rely on external computation for state estimation, trajectory planning or control [13], [15], [16]. Additionally, GPS [9], [19], [11], [16] or motioncapture systems [13], [12] are often used for state estimation, either only while patrolling or throughout the entire task.…”

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)

157

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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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“…Therefore, the drone needs to place the landing mark in the landing area before landfall. Vlantis et al [6] studies the problem of landing a quadrotor on an inclined moving platform. The aerial robot employs a forward-looking on-board camera to detect and observe the landing platform, which is carried by a mobile robot moving independently on an inclined surface.…”

Section: Introductionmentioning

confidence: 99%

Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

Yang

Zhang

et al. 2018

Electronics

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Abstract:With the popularization and wide application of drones in military and civilian fields, the safety of drones must be considered. At present, the failure and drop rates of drones are still much higher than those of manned aircraft. Therefore, it is imperative to improve the research on the safe landing and recovery of drones. However, most drone navigation methods rely on global positioning system (GPS) signals. When GPS signals are missing, these drones cannot land or recover properly. In fact, with the help of optical equipment and image recognition technology, the position and posture of the drone in three dimensions can be obtained, and the environment where the drone is located can be perceived. This paper proposes and implements a monocular vision-based drone autonomous landing system in emergencies and in unstructured environments. In this system, a novel map representation approach is proposed that combines three-dimensional features and a mid-pass filter to remove noise and construct a grid map with different heights. In addition, a region segmentation is presented to detect the edges of different-height grid areas for the sake of improving the speed and accuracy of the subsequent landing area selection. As a visual landing technology, this paper evaluates the proposed algorithm in two tasks: scene reconstruction integrity and landing location security. In these tasks, firstly, a drone scans the scene and acquires key frames in the monocular visual simultaneous localization and mapping (SLAM) system in order to estimate the pose of the drone and to create a three-dimensional point cloud map. Then, the filtered three-dimensional point cloud map is converted into a grid map. The grid map is further divided into different regions to select the appropriate landing zone. Thus, it can carry out autonomous route planning. Finally, when it stops upon the landing field, it will start the descent mode near the landing area. Experiments in multiple sets of real scenes show that the environmental awareness and the landing area selection have high robustness and real-time performance.

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Quadrotor landing on an inclined platform of a moving ground vehicle

Cited by 67 publications

References 12 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

Vision-based autonomous quadrotor landing on a moving platform

Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

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