Vision-Based Landing of a Simulated Unmanned Aerial Vehicle with Fast Reinforcement Learning

Shaker, Marwan; Smith, Mark N.R.; Yue, Shigang; Duckett, Tom

doi:10.1109/est.2010.14

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…Shaker et al [11] suggested another approach: reinforcement learning. In this approach, the UAV agent learns and adapts its behaviour when required.…”

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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“…Shaker et al [11] suggested another approach: reinforcement learning. In this approach, the UAV agent learns and adapts its behaviour when required.…”

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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“…In 2018, Anwar and Raychowdhury successfully made an unmanned aerial vehicle (UAV) learn to fly in a real environment via end-to-end deep reinforcement learning using monocular images [ 14 ]. Shaker and Smith presented a fast reinforcement learning algorithm for an unmanned aerial vehicle to learn how to automatically land using visual information [ 15 ]. However, most of these works focused on diminutive UAVs and quadcopters, which have simpler structures and are easier to control than complicated and sluggish civil aircraft.…”

Section: Introductionmentioning

confidence: 99%

Learning to Have a Civil Aircraft Take Off under Crosswind Conditions by Reinforcement Learning with Multimodal Data and Preprocessing Data

Liu

Dai

Zhao

2021

Sensors

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Autopilot technology in the field of aviation has developed over many years. However, it is difficult for an autopilot system to autonomously operate a civil aircraft under bad weather conditions. In this paper, we present a reinforcement learning (RL) algorithm using multimodal data and preprocessing data to have a civil aircraft take off autonomously under crosswind conditions. The multimodal data include the common flight status and visual information. The preprocessing is a new design that maps some flight data by nonlinear functions based on the general flight dynamics before these data are fed into the RL model. Extensive experiments under different crosswind conditions with a professional flight simulator demonstrate that the proposed method can effectively control a civil aircraft to take off under various crosswind conditions and achieve better performance than trials without visual information or preprocessing data.

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“…The control of multi-rotor UAVs for autonomous landing on a moving platform has been addressed in several lines of research: Position-Based Visual Servoing (PBVS) [1][2], Image-Based Visual Servoing (IBVS) [3] [4], tethered solutions [5][6] [7] [8], optic flow methods [9][10], high-speed landing with communication between moving platform and the UAV [11], bio-inspired [12] or machine learning based techniques [13] [14]. (i) Position-Based Visual Servoing extracts the position of the target, relative to the UAV and tracks the target in movement.…”

Section: Introductionmentioning

confidence: 99%

“…Non-classical approaches have also been tested, such as the use of artificial neural networks to detect a suitable landing zone [13]. Other learning techniques, such as reinforcement learning, have proven to have a high potential in the autonomous landing field, but were only tested in simulation and with a static platform [14].…”

Section: Introductionmentioning

confidence: 99%

Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles

Rodríguez-Ramos

Sampedro

Bavle

et al. 2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Fully autonomous landing on moving platforms poses a problem of importance for Unmanned Aerial Vehicles (UAVs). Current approaches are usually based on tracking and following the moving platform by means of several techniques, which frequently lack performance in real applications.The aim of this paper is to prove a simple landing strategy is able to provide practical results. The presented approach is based on three stages: estimation, prediction and fast landing. As a preliminary phase, the problem is solved for a particular case of the IMAV 2016 competition. Subsequently, it is extended to a more generic and versatile approach. A thorough evaluation has been conducted with simulated and real flight experiments. Simulations have been performed utilizing Gazebo 6 and PX4 Software-In-The-Loop (SITL) and real flight experiments have been conducted with a custom quadrotor and a moving platform in an indoor environment.The authors would like to thank the Spanish Ministry of Science MICYT DPI2014-60139-R for project funding. The LAL UPM and the MONCLOA Campus of International Excellence are also acknowledged for the funding of the predoctoral contract of one of the authors.All authors are with the Computer Vision and Aerial Robotics (CVAR) Group, Centre for Automation and Robotics (UPM-CSIC), Calle José Gutiérrez Abascal 2, Madrid (Spain).

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Vision-Based Landing of a Simulated Unmanned Aerial Vehicle with Fast Reinforcement Learning

Cited by 18 publications

References 15 publications

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

Learning to Have a Civil Aircraft Take Off under Crosswind Conditions by Reinforcement Learning with Multimodal Data and Preprocessing Data

Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles

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