• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.54-60
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• 2009

In this paper, a precision landing approach is implemented based on real-time image processing. A full-scale landmark for automatic landing is used. canny edge detection method is applied to identify the outside quadrilateral while circular hough transform is used for the recognition of inside circle. Position information on the ground landmark is uplinked to the unmanned helicopter via ground control computer in real time so that the unmanned helicopter control the air vehicle for accurate landing approach. Ground test and a couple of flight tests for autonomous landing approach show that the image processing and automatic landing operation system have good performance for the landing approach phase at the altitude of $20m{\sim}1m$ above ground level.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.377-382
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• 2006

Precision approach and landing of aircraft in a remote landing zone autonomously present several challenges. Firstly, the exact location, orientation and elevation of the landing zone are not always known; secondly, the accuracy of the navigation solution is not always sufficient for this type of precision maneuver if there is no DGPS availability within close proximity. This paper explores an alternative approach for estimating the navigation parameters of the aircraft to the landing area using only time-differenced GPS carrier phase measurement and range measurements from a vision system. Distinct ground landmarks are marked before the landing zone. The positions of these landmarks are extracted from the vision system then the ranges relative to these locations are used as measurements for the extended Kalman filter (EKF) in addition to the precise time-differenced GPS carrier phase measurements. The performance of this navigation algorithm is demonstrated using simulation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.6
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• pp.458-464
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• 2013

This paper presents an algorithm for autonomous landing approach of a unmanned aerial vehicle. The main purpose of the autonomous landing approach in this study is to help a safe landing at night. From any initial position of the aircraft when this function is engaged, a flight path command is generated from the initial position. The shortest combination of an initial circular arc, a straight line segment, and a final circular arc is chosen for the flight path that will lead the aircraft to one end of runway for a landing. The algorithm is initially validated through numerous simulations with various initial conditions of aircraft. Then it is successfully validated through a number of flight tests.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.485-492
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• 2021

Vision-based autonomous precision landing technology for UAVs requires precise position estimation and landing guidance technology. Also, for safe landing, it must be designed to determine the safety of the landing point against ground obstacles and to guide the landing only when the safety is ensured. In this paper, we proposes vision-based navigation, and algorithms for determining the safety of landing point to perform autonomous precision landings. To perform vision-based navigation, CNN technology is used to detect landing pad and the detection information is used to derive an integrated navigation solution. In addition, design and apply Kalman filters to improve position estimation performance. In order to determine the safety of the landing point, we perform the obstacle detection and position estimation in the same manner, and estimate the speed of the obstacle using LSM. The collision or not with the obstacle is determined based on the CPA calculated by using the estimated state of the obstacle. Finally, we perform flight test to verify the proposed algorithm.

• ETRI Journal
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• v.45 no.5
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• pp.746-757
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• 2023

This study presents a novel safe landing algorithm for urban drone deliveries. The rapid advancement of drone technology has given rise to various delivery services for everyday necessities and emergency relief efforts. However, the reliability of drone delivery technology is still insufficient for application in urban environments. The proposed approach uses the "landing angle control" method to allow the drone to land vertically and a rapidly exploring random tree-based collision avoidance algorithm to generate safe and efficient vertical landing paths for drones while avoiding common urban obstacles like trees, street lights, utility poles, and wires; these methods allow for precise and reliable urban drone delivery. We verified the approach within a Gazebo simulation operated through ROS using a six-degree-of-freedom drone model and sensors with similar specifications to actual models. The performance of the algorithms was tested in various scenarios by comparing it with that of stateof-the-art 3D path planning algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.912-918
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• 2016

This paper presents various strategies for humanoid vehicle driving and egress tasks. For driving, a tele-operating system that controls a robot based on a human operator's commands is built. In addition, an autonomous assistant module is developed for the operator. Normal position control can result in severe damage to robots when they egress from vehicles. To prevent this problem, another approach that mixes various joint control techniques is adopted in this study. Additionally, a footplate is newly designed and attached to the vehicle floor for the ground landing phase of the egress task. The attached plate enables the robot to step down onto the ground in a safe manner. For stable locomotion, a balance controller is designed for the humanoid. For the design of the controller, the robot is modeled using an inverted pendulum that consists of a spring and a damper. Then, a state feedback controller (with pole placement and a state observer) is built based on the simplified model. Many approaches that are presented in this paper were successfully applied to a full-sized humanoid, DRC-HUBO+, in the DARPA Robotics Challenge Finals, which were held in the United States in 2015.