Autonomous landing on a moving vehicle with an unmanned aerial vehicle

Báča, Tomáš; Štěpán, Petr; Spurný, Vojtěch; Heřt, Daniel; Pěnička, Robert; Saska, Martin; Thomas, Justin; Loianno, Giuseppe; Kumar, Vijay

doi:10.1002/rob.21858

Cited by 125 publications

(64 citation statements)

References 35 publications

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“…Baca et al [15] were able to detect a moving car at 15 km/h, predict its future movement and attach to it. To achieve it they equipped the UAV with onboard sensors and a computer, which detects the car using a monocular camera and predicts the car future movement using a nonlinear motion model.…”

Section: Category Iii-landing On a Shipmentioning

confidence: 99%

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

et al. 2019

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Over the last few years, several researchers have been developing protocols and applications in order to autonomously land unmanned aerial vehicles (UAVs). However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications such as package retrieval and delivery or the compact landing of UAV swarms. Therefore, in this work, a solution for high precision landing based on the use of ArUco markers is presented. In the proposed solution, a UAV equipped with a low-cost camera is able to detect ArUco markers sized 56 × 56 cm from an altitude of up to 30 m. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. The proposal was evaluated and validated using both the ArduSim simulation platform and real UAV flights. The results show an average offset of only 11 cm from the target position, which vastly improves the landing accuracy compared to the traditional GPS-based landing, which typically deviates from the intended target by 1 to 3 m.

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Section: Category Iii-landing On a Shipmentioning

confidence: 99%

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

et al. 2019

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“…They are the demonstration or proof-of-concept that could light a path toward more practical and realistic solutions [ 7 , 8 ]. Some of the recent studies have carried out flight tests in real-world environments where well-illuminated scenes favor the vision systems [ 9 , 10 ]. With the proliferation of UAV applications, there emerges an increasing need to operate UAVs at nighttime, benefiting from less airspace traffic and less human-activity-based interferences.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Lin

Jin

Chen

2021

Sensors

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Landing an unmanned aerial vehicle (UAV) autonomously and safely is a challenging task. Although the existing approaches have resolved the problem of precise landing by identifying a specific landing marker using the UAV’s onboard vision system, the vast majority of these works are conducted in either daytime or well-illuminated laboratory environments. In contrast, very few researchers have investigated the possibility of landing in low-illumination conditions by employing various active light sources to lighten the markers. In this paper, a novel vision system design is proposed to tackle UAV landing in outdoor extreme low-illumination environments without the need to apply an active light source to the marker. We use a model-based enhancement scheme to improve the quality and brightness of the onboard captured images, then present a hierarchical-based method consisting of a decision tree with an associated light-weight convolutional neural network (CNN) for coarse-to-fine landing marker localization, where the key information of the marker is extracted and reserved for post-processing, such as pose estimation and landing control. Extensive evaluations have been conducted to demonstrate the robustness, accuracy, and real-time performance of the proposed vision system. Field experiments across a variety of outdoor nighttime scenarios with an average luminance of 5 lx at the marker locations have proven the feasibility and practicability of the system.

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“…Autonomous landing feature is a major component in fully autonomous unmanned aerial vehicles (UAVs), as it is the precision of landing what is important in autonomous docking of any UAV platform, as stated in [ 1 ], into recharging stations in missions requiring repeated flight operations, pick and place tasks, and in executing repetitive behavior of the UAVs. Let alone, it is a difficult task, and gets even more complicated when the target landing location is moving, as UAVs are recently used to deliver different types of loads in dense environments such as cities, due to their manoeuvrability and possibility of vertical take-off and landing (VTOL) [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Altitude Measurement-Based Optimization of the Landing Process of UAVs

Horla

Giernacki

Cieślak

et al. 2021

Sensors

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The paper addresses the loop shaping problem in the altitude control of an unmanned aerial vehicle to land the flying robot with a specific landing scenario adopted. The proposed solution is optimal, in the sense of the selected performance indices, namely minimum-time, minimum-energy, and velocity-penalized related functions, achieving their minimal values, with numerous experiments conducted throughout the development and preparation to the Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). A novel approach to generation of a reference altitude trajectory is presented, which is then tracked in a standard, though optimized, control loop. Three landing scenarios are considered, namely: minimum-time, minimum-energy, and velocity-penalized landing scenarios. The experimental results obtained with the use of the Simulink Support Package for Parrot Minidrones, and the OptiTrack motion capture system proved the effectiveness of the proposed approach.

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Autonomous landing on a moving vehicle with an unmanned aerial vehicle

Cited by 125 publications

References 35 publications

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Altitude Measurement-Based Optimization of the Landing Process of UAVs

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