Autonomous Landing of UAV Based on Artificial Neural Network Supervised by Fuzzy Logic

Souza, João Pedro Carvalho de; Marcato, André L. M.; Aguiar, Eduardo Pestana de; Jucá, Marco Aurélio; Teixeira, Alexandre Menezes

doi:10.1007/s40313-019-00465-y

Cited by 22 publications

(13 citation statements)

References 23 publications

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“…De Souza et al [16] developed a autonomous landing system based on Artificial Neural Network (ANN) supervised by Fuzzy Mamdani Logic. Their method introduced low computational complexity while maintaining the characteristics and intelligence of the fuzzy logic controller.…”

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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“…Regardless of the good performance by PID controllers, these still need to be adaptive for uncertain conditions. To achieve this purpose, PID control was extended further to fuzzy adaptive PID [ 23 , 24 ], and the authors of reference [ 16 , 25 ] presented a fuzzy logic-based UAV tracking and landing using computer vision.…”

Section: Introductionmentioning

confidence: 99%

UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy

Xie

Peng

Wang

et al. 2020

Sensors

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Unmanned aerial vehicle (UAV) autonomous tracking and landing is playing an increasingly important role in military and civil applications. In particular, machine learning has been successfully introduced to robotics-related tasks. A novel UAV autonomous tracking and landing approach based on a deep reinforcement learning strategy is presented in this paper, with the aim of dealing with the UAV motion control problem in an unpredictable and harsh environment. Instead of building a prior model and inferring the landing actions based on heuristic rules, a model-free method based on a partially observable Markov decision process (POMDP) is proposed. In the POMDP model, the UAV automatically learns the landing maneuver by an end-to-end neural network, which combines the Deep Deterministic Policy Gradients (DDPG) algorithm and heuristic rules. A Modular Open Robots Simulation Engine (MORSE)-based reinforcement learning framework is designed and validated with a continuous UAV tracking and landing task on a randomly moving platform in high sensor noise and intermittent measurements. The simulation results show that when the moving platform is moving in different trajectories, the average landing success rate of the proposed algorithm is about 10% higher than that of the Proportional-Integral-Derivative (PID) method. As an indirect result, a state-of-the-art deep reinforcement learning-based UAV control method is validated, where the UAV can learn the optimal strategy of a continuously autonomous landing and perform properly in a simulation environment.

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“…In some industrial applications, PID controllers are used because of their simple structure and the easy control design in the case when a process is linear or can be linearized at an operating point, however, there are processes that are not linear in the whole operating range but piecewise linearizable, for example, plants with nonlinear characteristics, chemical processes, robots, and UAVs (SOUZA et al, 2019). The Fuzzy Logic System (FLS) is a good alternative of control that can manage highly nonlinear systems with uncertainties and do not need an accurate vehicle mathematical model (PRECUP; HELLENDOORN, 2011), which can be a problem for classical controllers.…”

Section: Introductionmentioning

confidence: 99%

“…The Fuzzy Logic System (FLS) is a good alternative of control that can manage highly nonlinear systems with uncertainties and do not need an accurate vehicle mathematical model (PRECUP; HELLENDOORN, 2011), which can be a problem for classical controllers. According to (SOUZA et al, 2019), the FLS is real-time expert system implementing human experiences and knowledge, which can not be realized by PID and, in addition, the FLSs are the heuristic modular way of defining any non-linear control system, that flexibility is absent in PID.…”

Section: Introductionmentioning

confidence: 99%

“…Although, these multiple concurrent controllers approaches are not practical and simplified alternative when the goal is the embedded use. The authors in (SOUZA et al, 2019) present an alternative method to train a multilayer perceptron neural network based on fuzzy Mamdani logic to control the landing of a UAV on an artificial marker. The works Revista Mundi, Engenharia e Gestão, Paranaguá, PR, v. 5, n. 6, p. 277-01, 277-13, 2020DOI: 10.21575/25254782rmetg2020vol5n61286 (SOUZA et al, 2019OLIVARES-MENDEZ;KANNAN;VOOS, 2015;BENAVIDEZ et al, 2014) adopted the markers composition for landing spot making it an interesting alternative since different sizes are proposed for detection at distinct vehicle height levels.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Landing of Uav: A Comparison Between Type-1 and Type-2 Fuzzy Logic Systems

et al. 2020

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The Unmanned Aerial Vehicles (UAV) are an important technology with multiple applications. It is an object of study for researchers aiming to improve the performance of these vehicles, especially in flight stages as the landing. Therefore, this paper presents a method for the landing of a UAV based on Type-2 Fuzzy Logic System considering static targets. The advantage of this process is more precision and accuracy compared with Type-1 Fuzzy Logic System.

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Autonomous Landing of UAV Based on Artificial Neural Network Supervised by Fuzzy Logic

Cited by 22 publications

References 23 publications

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy

Autonomous Landing of Uav: A Comparison Between Type-1 and Type-2 Fuzzy Logic Systems

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