Query: autonomous landing

title: An Onboard Vision Based System for Autonomous Landing of a Low Cost Quadrotor on a Novel Landing Pad
abstract: In this paper, an onboard vision based system for the autonomous landing of a low cost quadrotor is presented. A novel landing pad with different optical markers sizes is carefully designed to be robustly recognized at different distances. To provide reliable pose information in a GPS (Global Positioning System) denied environment, a vision algorithm for real time landing pad recognition and pose estimation is implemented. The dynamic model of the quadrotor is established and a system scheme for autonomous landing control is presented. A series of autonomous flights have been successfully performed, and a video of the experiment is available online. The efficiency and accuracy of the presented vision based system is demonstrated by using its position and attitude estimates as control inputs for the autonomous landing of a self customized quadrotor.

title: Recent development of autonomous GNC technologies for small celestial body descent and landing
abstract: As small celestial body exploration advances, higher requirements with regard to system safety and landing precision are proposed for future landing and sample return missions. However, due to limited prior information about the target, the complex dynamics environment, and significant time delay, performing a descent and landing on the small body surface is challenging. Among all the techniques required for achieving a safe landing, onboard guidance, navigation, and control (GNC) is of paramount importance in determining mission success. In this paper, a systematic survey of the autonomous GNC technologies for descent and landing on small bodies is carried out. First, based on an analysis of the technical challenges in the process, an overview of typical small body landing and sample return missions is given. Then, an elaboration of the state of the art GNC technologies is presented. Specifically, autonomous navigation methods in unknown environments with highly nonlinear dynamics are introduced. Descent guidance and control algorithms that take into account landing performance optimization and system robustness against model uncertainties are discussed. Touchdown dynamics and control methods proposed for precise and safe surface contact under weak gravity are analyzed. And safe strategies for onboard detected emergencies such as collision threats and system malfunctions are explained. Besides the prevalent methods, innovative techniques with respect to observability based optimization, edge curve matching, online landing site selection, collision probability based hazard avoidance, and trajectory curvature guidance proposed for improving system safety and landing performance are elucidated. At last, based on the growing system autonomy and operational complexity demands, a prospect of future research directions for small body GNC technologies is given.

title: Autonomous landing on a moving vehicle with an unmanned aerial vehicle
abstract: This paper addresses the perception, control and trajectory planning for an aerial platform to identify and land on a moving car at 15 km/h. The hexacopter Unmanned Aerial Vehicle (UAV) equipped with onboard sensors and a computer, detects the car using a monocular camera and predicts the car future movement using a nonlinear motion model. While following the car, the UAV lands on its roof, and it attaches itself using magnetic legs. The proposed system is fully autonomous from takeoff to landing. Numerous field tests were conducted throughout the year long development and preparations for the MBZIRC 2017 competition, for which the system was designed. We propose a novel control system in which a Model Predictive Controller is used in real time to generate a reference trajectory for the UAV, which are then tracked by the nonlinear feedback controller. This combination allows to track predictions of the car motion with minimal position error. The evaluation presents three successful autonomous landings during the MBZIRC 2017, where our system achieved the fastest landing among all competing teams. *http:/mrs.felk.cvut.cz +http:/www.grasp.upenn.edu

title: Autonomous Landing of UAV Based on Artificial Neural Network Supervised by Fuzzy Logic
abstract: Autonomous Unmanned Aerial Vehicles (UAVs) become an important field of research in which multiple applications can be designed, such as surveillance, deliveries, and others. Thus, studies aiming to improve the performance of these vehicles are being proposed: from new sensing solutions to more robust control techniques. Additionally, the autonomous UAV has challenges in flight stages as the landing. This procedure needs to be performed safely with a reduced error margin in static and dynamic targets. To solve this imperative issue, many applications with computer vision and control theory have been developed. Therefore, this paper presents 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 advantage of this method is the reduction in computational complexity while maintaining the characteristics and intelligence of the fuzzy logic controller. Results are presented with simulation and real tests for static and dynamic landing spots. For the real experiments, a quadcopter with an onboard computer and ROS is used.

title: A software platform for vision based UAV autonomous landing guidance based on markers estimation
abstract: The paper concentrated on descripting the methods of UAV autonomous landing on moving target. GPS navigation and vision based navigation were employed during different stage of autonomous landing in the simulation environment and virtual reality. Uncertain markers estimation is the main step for UAV autonomous landing. It contains the convex hull transformation, interference preclusion, ellipse fitting and specific feature matching. Furthermore, the complete visual measurement program and guidance strategy were proposed in this paper. Considerable comprehensive experiments indicated the significance and feasibility of method of vision based UAV autonomous landing on moving target.

title: Robust Visual Aided Autonomous Takeoff, Tracking, and Landing of a Small UAV on a Moving Landing Platform for Life Long Operation
abstract: Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi robot system requires the autonomous takeoff, tracking, and landing of the UAV on the moving UGV. Furthermore, it needs to be robust and capable of life long operation. In this paper, we present an autonomous system that enables a UAV to take off autonomously from a moving landing platform, locate it using visual cues, follow it, and robustly land on it. The system relies on a finite state machine, which together with a novel re localization module allows the system to operate robustly for extended periods of time and to recover from potential failed landing maneuvers. Two approaches for tracking and landing are developed, implemented, and tested. The first variant is based on a novel height adaptive PID controller that uses the current position of the landing platform as the target. The second one combines this height adaptive PID controller with a Kalman filter in order to predict the future positions of the platform and provide them as input to the PID controller. This facilitates tracking and, mainly, landing. Both the system as a whole and the re localization module in particular have been tested extensively in a simulated environment (Gazebo) We also present a qualitative evaluation of the system on the real robotic platforms, demonstrating that our system can also be deployed on real robotic platforms. For the benefit of the community, we make our software open source.

title: Zero visibility autonomous landing of quadrotors on underway ships in a sea state
abstract: Unmanned aerial vehicles (UAV) are a valuable resource and have many applications in marine environments. The UAV's capabilities can be further augmented through increased autonomy. One potential area for more autonomy is landing on the stern of underway ships. This requires good position estimates that are independent of GPS and in many harsh environments, vision. We demonstrate a position estimation and tracking technique that uses peer to peer acoustic range measurements coupled with the relative inertial measurement unit estimates from the ship and UAV to create a robust state estimation. A Stewart Gough platform is used to emulate the motions of a ship stern, where the UAV would land, in different sea states and a state of the art motion capture system provides the ground truth positioning for the UAV and the Stewart Gough platform. We demonstrate a pose estimate with a mean squared error (MSE) of 9.41 cm (23.8% of landing area length) and a "ready to land" position tracker with an MSE of 12.05 cm (30.4% of landing area length) The proposed extended Kalman Filter used to fuse the measurements is more than adequate. The zero visibility autonomous landing algorithm works well and the next stage will be validation at sea.

title: Autonomous Adaptive Control System for Airplane Landing

title: Fuzzy Logic Based Robust and Autonomous Safe Landing for UAV Quadcopter
abstract: Recent years, unmanned aerial vehicles (UAVs) have been used to perform various tasks such as surveillance, monitoring, rescue, photography, and security. Quadcopter configuration of UAV is most common due to its simplicity, stability, and versatile controllability. Quadcopter landing is most crucial part of the overall operating process. It needs a lot of practice and effort for a safe landing. Therefore, a fuzzy logic based safe landing system is proposed in this paper. This paper includes a landing system based on laser rangefinder, Arduino Mega MCU, and Pixhawk flight controller. The lookup table technique is used to implement fuzzy logic inside Arduino Mega. This technique takes very small execution time for data processing in fuzzy logic, which is essential for high speed data processing and updating. Autonomous landing process can be triggered and override at any time using remote controller. Furthermore, various tests are performed on a quadcopter to verify the feasibility of proposed algorithm.

title: A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform
abstract: 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 designed 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, which has been integrated in 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.