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

Rodríguez-Ramos, Alejandro; Sampedro, Carlos; Bavle, Hriday; Milosevic, Zorana; Garcia-Vaquero, Alejandro; Campoy, Pascual

doi:10.1109/icuas.2017.7991438

Cited by 16 publications

(9 citation statements)

References 26 publications

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“…A replica of the simulated environment has been created for the real-flight testing phase. The which was designed and built for previous works on autonomous landing [34,36], is able to move in linear periodic trajectories (with rails) and in arbitrary trajectories. The selected UAV platform was a Parrot Bebop 4 due to its small size, robust control and higher flight velocity (see Figure 3b).…”

Section: Real-flight Testing Phasementioning

confidence: 99%

“…Nevertheless, these fields of application impose enormous constraints for normal operation tasks such as taking-off, navigation, object detection, environment interaction or landing. Thus, due to their level of complexity, researchers have approached these tasks separately in a diverse set of research lines [18,24,35,23,38] and international competitions [34,12], e.g. International Micro-Air Vehicles competition (IMAV) 1 .…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Real-flight Testing Phasementioning

confidence: 99%

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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“…However, it relies on the landing target being visible throughout the task since it relies solely on visual information. To deal with scenarios such as intermittent loss of landing target, model-based approaches [ 24 , 25 , 26 ] were proposed to predict the trajectory of the landing target. Alternative solutions include attachment of additional sensors on the moving target (e.g., GPS receivers [ 27 ]).…”

Section: Introductionmentioning

confidence: 99%

Autonomous Quadcopter Landing on a Moving Target

Gautam

Singh

Sujit

et al. 2022

Sensors

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Autonomous landing on a moving target is challenging because of external disturbances and localization errors. In this paper, we present a vision-based guidance technique with a log polynomial closing velocity controller to achieve faster and more accurate landing as compared to that of the traditional vertical landing approaches. The vision system uses a combination of color segmentation and AprilTags to detect the landing pad. No prior information about the landing target is needed. The guidance is based on pure pursuit guidance law. The convergence of the closing velocity controller is shown, and we test the efficacy of the proposed approach through simulations and field experiments. The landing target during the field experiments was manually dragged with a maximum speed of 0.6 m/s. In the simulations, the maximum target speed of the ground vehicle was 3 m/s. We conducted a total of 27 field experiment runs for landing on a moving target and achieved a successful landing in 22 cases. The maximum error magnitude for successful landing was recorded to be 35 cm from the landing target center. For the failure cases, the maximum distance of vehicle landing position from target boundary was 60 cm.

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“…Aerostack has been used in the development of complex robotic systems related, for example, to natural user interfaces, 11 surface inspection, 12 coordinated multi-robot systems, 13 landing on moving platforms, 14 search and rescue missions, 15 and altitude estimation in complex dynamic environments. 16 Figure 1 shows the reference architecture of Aerostack as it is defined in the last release (version 3.0).…”

Section: Introductionmentioning

confidence: 99%

Building the executive system of autonomous aerial robots using the Aerostack open-source framework

Molina

Carrera

Camporredondo

et al. 2020

International Journal of Advanced Robotic Systems

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A variety of open-source software tools are currently available to help building autonomous mobile robots. These tools have proven their effectiveness in developing different types of robotic systems, but there are still needs related to safety and efficiency that are not sufficiently covered. This article describes recent advances in the Aerostack software framework to address part of these needs, which may become critical in the case of aerial robots. The article describes a software tool that helps to develop the executive system, an important component of the control architecture whose characteristics significantly affect the quality of the final autonomous robotic system. The presented tool uses an original solution for execution control that aims at simplifying mission specification and protecting against errors, considering also the efficiency needs of aerial robots. The effectiveness of the tool was evaluated by building an experimental autonomous robot. The results of the evaluation show that it provides significant benefits about usability and reliability with acceptable development effort and computational cost. The tool is based on Robot Operating System and it is publicly available as part of the last release of the Aerostack software framework (version 3.0).

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

Cited by 16 publications

References 26 publications

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

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

Autonomous Quadcopter Landing on a Moving Target

Building the executive system of autonomous aerial robots using the Aerostack open-source framework

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