Landing on unmanned surface vehicles (USV) autonomously is a critical task for unmanned aerial vehicles (UAV) due to complex environments. To solve this problem, an autonomous landing method is proposed based on a multi-level marker and linear active disturbance rejection control (LADRC) in this study. A specially designed landing board is placed on the USV, and ArUco codes with different scales are employed. Then, the landing marker is captured and processed by a camera mounted below the UAV body. Using the efficient perspective-n-point method, the position and attitude of the UAV are estimated and further fused by the Kalman filter, which improves the estimation accuracy and stability. On this basis, LADRC is used for UAV landing control, in which an extended state observer with adjustable bandwidth is employed to evaluate disturbance and proportional-derivative control is adopted to eliminate control error. The results of simulations and experiments demonstrate the feasibility and effectiveness of the proposed method, which provides an effective solution for the autonomous recovery of unmanned systems.
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http://dx.doi.org/10.3390/s24051645 | DOI Listing |
Heliyon
January 2025
Swansea University, Swansea, Wales, SA2 8PP, UK.
Urban Air Transportation (UAT) encompasses private aircraft, air taxis, and specialized missions. These missions include aerial sightseeing, logistics transportation, emergency response, and anti-terrorism operations. They impose stringent requirements on advanced air mobility (AAM) aircraft.
View Article and Find Full Text PDFFront Robot AI
December 2024
School of Electrical and Electronic Engineering, University of Sheffield, Sheffield, United Kingdom.
This paper proposes a solution to the challenging task of autonomously landing Unmanned Aerial Vehicles (UAVs). An onboard computer vision module integrates the vision system with the ground control communication and video server connection. The vision platform performs feature extraction using the Speeded Up Robust Features (SURF), followed by fast Structured Forests edge detection and then smoothing with a Kalman filter for accurate runway sidelines prediction.
View Article and Find Full Text PDFSci Rep
December 2024
Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, 110 8th St, Troy, 12180, NY, USA.
Front Robot AI
November 2024
Centre for Autonomous and Cyber-Physical Systems, Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield, United Kingdom.
This paper presents a novel optimal trajectory-shaping control concept for a planetary hopping robot. The hopping robot suffers from uncontrolled in-flight and undesired after-landing motions, leading to a position drift at landing. The proposed concept thrives on the Generalized Vector Explicit (GENEX) guidance, which can generate and shape the optimal trajectory and satisfy the end-point constraints like the impact angle of the velocity vector.
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