Research on Adaptive Guidance Technology of UAV Ship Landing System Based on Net Recovery

Wang, Kai; Chunzhen, Sun; Jiang, Yi

doi:10.1016/j.proeng.2014.12.637

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…where if the UAV and the vessel are facing each other, the symbol "+" is selected, but if not, the symbol "−" is selected. In formula (14), the angle between the UAV flight speed vector and the ship's navigation speed vector cannot be equal to 90 • .…”

Section: Approach Landing Trajectory Estimation Between Drone and Vesselmentioning

confidence: 99%

“…where m L refers to the amount of fuel consumed by the drone unit path. It is worth noting that, if L II ≥ L I + L III + L IV , the approximate calculation Formula (21) can be used to replace the exact calculation formula of the landing trajectory approach L (14).…”

Section: Approach Landing Trajectory Estimation Between Drone and Vesselmentioning

confidence: 99%

“…By simulating the dynamics of different sea conditions and vessel decks and using a well-designed computer vision system, the ship deck is measured related to the posture of the drone. Wang Kai et al [14] proposed a network-based UAV ship with a self-adaptive guidance system. By controlling the line-of-sight angle to track the angle of the flight path, the angle guidance law and the reverse guidance law are introduced.…”

Section: Introductionmentioning

confidence: 99%

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Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel

Tan

Yang

et al. 2019

Applied Sciences

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The location, velocity, and flight path angle of an autonomous unmanned aerial vehicle (UAV) landing on a moving vessel are key factors for an optimal landing trajectory. To tackle this challenge, this paper proposes a method for calculating the optimal approach landing trajectory between an UAV and a small vessel. A numerical approach (iterative method) is used to calculate the optimal approach landing trajectory, and the initial lead is introduced in the calculation process of the UAV trajectory for the inclination and heading angle for accuracy improvement, so that the UAV can track and calculate the optimal landing trajectory with high precision. Compared with the variational method, the proposed method can calculate an optimal turning direction angle for the UAV during the landing. Simulation experiments verify the effectiveness of the proposed algorithm and give optimal initialization values.

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Section: Approach Landing Trajectory Estimation Between Drone and Vesselmentioning

confidence: 99%

Section: Approach Landing Trajectory Estimation Between Drone and Vesselmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel

Tan

Yang

et al. 2019

Applied Sciences

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“…According to Kai, Chunzhen & Yi (2015), depending on the characteristics of weight, yield, flight range and altitude, UAVs can be classified into groups as indicated in Tables 1 and 2. UAVs can also be classified in fixed wings or rotating wing.…”

Section: Categorymentioning

confidence: 99%

Computer vision systems in unmanned aerial vehicle: a review

Mendes

Borille

2019

jme

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Inspections in areas of difficult access or hostile to the human, pattern recognition, surveillance and monitoring, are some of the many applications in with Unmanned Aerial Vehicles (UAV), can be a solution, opening up new perspectives for the use of this technology. The navigation and the position of the UAVs can be made by autonomous method through the computational vision, which is a technology of construction of artificial systems capable of read information from images or any multidimensional data and making decisions. This work presents a review of the use of computer vision systems by UAVs, with a focus on its many applications. The main objective is to analyze the latest technologies used for the development of computer vision in UAVs, through the tools of data search, information storage and, mainly, processing and analysis of data. The researches encompasses a publication of recent works, 2011 onwards, from the Science Direct portal. For each work were analyzed the objectives, methodology and results. Based in this analysis, was made a comparison between the techniques and their challenges. From this, future outlook scenarios of UAVs using computational vision are mentioned.

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“…Indeed, these facts can be limiting for providing multi-rotor UAVs with the required level of autonomy in long-term missions. Due to this reason, the landing maneuver on a moving platform has been on the focus of several research lines for years [14,3,6,42,5,20,46,15].…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform

Rodríguez-Ramos

Sampedro

Bavle

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by the rapid innovation in all the technologies involved. In particular, deep learning techniques for motion control have recently taken a major qualitative step, since the successful application of Deep Q-Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide outstanding results with continuous state and action domains, which are a requirement in most of the robotics-related tasks. In this context, the research community is lacking the integration of realistic simulation systems with the reinforcement learning paradigm, enabling the application of deep reinforcement learning algorithms to the robotics field.In this paper, a versatile Gazebo-based reinforcement learning framework has been established and validated with a continuous UAV landing task. The UAV landing maneuver on a moving platform has been solved by means of the novel DDPG algorithm and our reinforcement learning framework. Several experiments have been performed in a wide variety of conditions for both simulated and real flights, demonstrating the generality of the approach. As an indirect result, a powerful work flow for robotics has been validated, where robots can learn in simulation and perform properly in real operation environments. To the best of the authors knowledge, this is the first work that addresses the continuous UAV landing maneuver on a moving platform by means of a state-of-the-art deep reinforcement learning algorithm, trained in simulation and tested in real flights.

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Research on Adaptive Guidance Technology of UAV Ship Landing System Based on Net Recovery

Cited by 11 publications

References 11 publications

Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel

Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel

Computer vision systems in unmanned aerial vehicle: a review

A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform

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