Vision-Based Autonomous Landing of a Multi-Copter Unmanned Aerial Vehicle using Reinforcement Learning

Lee, Seongheon; Shim, Taemin; Kim, Sung-Joong; Park, Jun‐Woo; Hong, Kyungwoo; Bang, Hyochoong

doi:10.1109/icuas.2018.8453315

Cited by 37 publications

(11 citation statements)

References 8 publications

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“…Here, the authors achieved autonomous landing using a single neural network, but, differently from our work, they significantly constrained the state space and designed a really specific reward function. In [28], the authors successfully trained a UAV to perform autonomous landing while keeping a constant descending speed. However, the method has access to a rich state representation (robot attitude, pixel distance from the marker in the camera frame, altitude) and the reward function is handcrafted and not general such as the one used in our work.…”

Section: Related Workmentioning

confidence: 99%

Sim-to-Real Quadrotor Landing via Sequential Deep Q-Networks and Domain Randomization

et al. 2020

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The autonomous landing of an Unmanned Aerial Vehicle (UAV) on a marker is one of the most challenging problems in robotics. Many solutions have been proposed, with the best results achieved via customized geometric features and external sensors. This paper discusses for the first time the use of deep reinforcement learning as an end-to-end learning paradigm to find a policy for UAVs autonomous landing. Our method is based on a divide-and-conquer paradigm that splits a task into sequential sub-tasks, each one assigned to a Deep Q-Network (DQN), hence the name Sequential Deep Q-Network (SDQN). Each DQN in an SDQN is activated by an internal trigger, and it represents a component of a high-level control policy, which can navigate the UAV towards the marker. Different technical solutions have been implemented, for example combining vanilla and double DQNs, and the introduction of a partitioned buffer replay to address the problem of sample efficiency. One of the main contributions of this work consists in showing how an SDQN trained in a simulator via domain randomization, can effectively generalize to real-world scenarios of increasing complexity. The performance of SDQNs is comparable with a state-of-the-art algorithm and human pilots while being quantitatively better in noisy conditions.

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Section: Related Workmentioning

confidence: 99%

Sim-to-Real Quadrotor Landing via Sequential Deep Q-Networks and Domain Randomization

et al. 2020

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“…The article [4] presents the possibility of using reinforcement learning to generate the control necessary for autonomous landing. In the learning phase a simulation environment was used.…”

Section: Previous Workmentioning

confidence: 99%

“…Summarizing the review, it is worth noting that in most works vision systems were able to process just a few frames per second. The authors of [4] pointed out that that this could be the reason the drone was oscillating during landing. Moreover, in [1] one of the mentioned challenges was the use of an energy-efficient platform to perform calculations in realtime.…”

Section: Previous Workmentioning

confidence: 99%

Vision based hardware-software real-time control system for autonomous landing of an UAV

Błachut¹,

Szolc²,

Wąsala³

et al. 2020

Preprint

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In this paper we present a vision based hardware-software control system enabling autonomous landing of a mul-tirotor unmanned aerial vehicle (UAV). It allows the detection of a marked landing pad in real-time for a 1280 x 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption.

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“…With an optimal policy, they have demonstrated a quadcopter autonomously landing in a large variety of simulated environments. A number of approaches based on adaptive neural networks have also been adopted to render the trajectory controller more robust and adaptive, ensuring that the controller is capable of guiding aircraft to a safe landing in the presence of various disturbances and uncertainties [22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

Autonomous Landing of a UAV on a Moving Platform Using Model Predictive Control

Feng

Zhang

Baek

et al. 2018

Drones

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Developing methods for autonomous landing of an unmanned aerial vehicle (UAV) on a mobile platform has been an active area of research over the past decade, as it offers an attractive solution for cases where rapid deployment and recovery of a fleet of UAVs, continuous flight tasks, extended operational ranges, and mobile recharging stations are desired. In this work, we present a new autonomous landing method that can be implemented on micro UAVs that require high-bandwidth feedback control loops for safe landing under various uncertainties and wind disturbances. We present our system architecture, including dynamic modeling of the UAV with a gimbaled camera, implementation of a Kalman filter for optimal localization of the mobile platform, and development of model predictive control (MPC), for guidance of UAVs. We demonstrate autonomous landing with an error of less than 37 cm from the center of a mobile platform traveling at a speed of up to 12 m/s under the condition of noisy measurements and wind disturbances.

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Vision-Based Autonomous Landing of a Multi-Copter Unmanned Aerial Vehicle using Reinforcement Learning

Cited by 37 publications

References 8 publications

Sim-to-Real Quadrotor Landing via Sequential Deep Q-Networks and Domain Randomization

Sim-to-Real Quadrotor Landing via Sequential Deep Q-Networks and Domain Randomization

Vision based hardware-software real-time control system for autonomous landing of an UAV

Autonomous Landing of a UAV on a Moving Platform Using Model Predictive Control

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