A Semi-Physical Platform for Guidance and Formations of Fixed-Wing Unmanned Aerial Vehicles

Yang, Jun; Thomas, Arun Geo; Singh, S. C.; Baldi, Simone; Wang, Ximan

doi:10.3390/s20041136

Cited by 14 publications

(11 citation statements)

References 35 publications

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“…These rules are derived as error gradient descent rules. A semi-physical platform for multiple fixed winged UAVs to control its formation is proposed in [33]. In [34], a detailed survey on inexpensive UAV platforms for infrastructure inspection is presented.…”

Section: U Wmentioning

confidence: 99%

UAVs-UGV Leader Follower Formation Using Adaptive Non-Singular Terminal Super Twisting Sliding Mode Control

et al. 2021

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Leader follower formation of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has found numerous applications such as surveillance of critical infrastructure, industrial automation and disaster management emergency. For completion of high precision group tasks, the choice of appropriate control mechanism is of utmost importance. In presence of environmental effects,external disturbances and parametric uncertainties in the UAVs and UGV models, the controller design process is a challenging task. In order to address the aforementioned problems and to ensure minimum tracking errors and fast convergence of the states, this article proposes an adaptive robust formation and trajectory tracking control scheme for a leader follower formation of UAVs and UGV using Non-Singular Terminal Super Twisting Sliding Mode Control Method.Adaptive compensators are derived based on Lyapunov function method and stability of the proposed controllers is guaranteed. Two variants of the control schemes namely Adaptive Super Twisting SMC (AST-SMC) and Adaptive Non-Singular Terminal Super Twisting SMC (ANSTS-SMC) are tested using numerical simulations performed in MATLAB/Simulink. From the results presented, and with the proposed ANSTS-SMC control scheme, the measured [X , Y ] tracking errors for leader, follower1 and follower2 UAVs are [0 , 0.01]m,[0.01 , 0.02]m and [0.01 , 0.02]m respectively, While with the AST-SMC method the peak [X , Y ] tracking errors for leader, follower1 and follower2 UAVs are [0.05 , 0.05]m,[0.1 , 0.2]m and [0.05 , 0.1]m respectively. The proposed leader follower formation can be effectively used to monitor solar/PV panels and cables in large solar parks.

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Section: U Wmentioning

confidence: 99%

UAVs-UGV Leader Follower Formation Using Adaptive Non-Singular Terminal Super Twisting Sliding Mode Control

et al. 2021

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“…During the last two decades, unmanned aerial vehicles have experienced enormous development [1,2]. Since unmanned aerial vehicles (UAVs) were released for general civil use, the number of incidents involving them have been constantly increasing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space

Dudczyk¹,

Czyba

Skrzypczyk

2022

Sensors

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The paper focuses on the problem of detecting unmanned aerial vehicles that violate restricted airspace. The main purpose of the research is to develop an algorithm that enables the detection, identification and recognition in 3D space of a UAV violating restricted airspace. The proposed method consists of multi-sensory data fusion and is based on conditional complementary filtration and multi-stage clustering. On the basis of the review of the available UAV detection technologies, three sensory systems classified into the groups of passive and active methods are selected. The UAV detection algorithm is developed on the basis of data collected during field tests under real conditions, from three sensors: a radio system, an ADS-B transponder and a radar equipped with four antenna arrays. The efficiency of the proposed solution was tested on the basis of rapid prototyping in the MATLAB simulation environment with the use of data from the real sensory system obtained during controlled UAV flights. The obtained results of UAV detections confirmed the effectiveness of the proposed method and theoretical expectations.

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“…Moreover, it is time-consuming and inefficient for multiple sorties by a single UAV to perform coverage survey tasks in large areas. Presently, it is becoming a trend for multiple UAV formations to accomplish tasks jointly [23].…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Collaboration to Survey Tibetan Antelopes in Hoh Xil

Huang

Han

Liu

et al. 2022

Drones

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Reducing the total mission time is essential in wildlife surveys owing to the dynamic movement of animals throughout their migrating environment and potentially extreme changes in weather. This paper proposed a multi-UAV path planning method for counting various flora and fauna populations, which can fully use the UAVs’ limited flight time to cover large areas. Unlike the current complete coverage path planning methods, based on sweep and polygon, our work encoded the path planning problem as the satisfiability modulo theory using a one-hot encoding scheme. Each instance generated a set of feasible paths at each iteration and recovered the set of shortest paths after sufficient time. We also flexibly optimized the paths based on the number of UAVs, endurance and camera parameters. We implemented the planning algorithm with four UAVs to conduct multiple photographic aerial wildlife surveys in areas around Zonag Lake, the birthplace of Tibetan antelope. Over 6 square kilometers was surveyed in about 2 h. In contrast, previous human-piloted single-drone surveys of the same area required over 4 days to complete. A generic few-shot detector that can perform effective counting without training on the target object is utilized in this paper, which can achieve an accuracy of over 97%.

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A Semi-Physical Platform for Guidance and Formations of Fixed-Wing Unmanned Aerial Vehicles

Cited by 14 publications

References 35 publications

UAVs-UGV Leader Follower Formation Using Adaptive Non-Singular Terminal Super Twisting Sliding Mode Control

UAVs-UGV Leader Follower Formation Using Adaptive Non-Singular Terminal Super Twisting Sliding Mode Control

Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space

Multi-UAV Collaboration to Survey Tibetan Antelopes in Hoh Xil

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