Hyoung Sik Choi

Vision-Based Detection and Tracking of Airborne Obstacles in a Cluttered Environment

This paper proposes an image processing algorithm for ‘sense-and-avoid’ of aerial vehicles in short-range at low altitude and shows flight experiment results. Since it can suppress the negative effects cause cluttered environment in image sequence such as the ground seen or sensitivity of threshold value during low-altitude flight, proposed algorithm has better performance of collision avoidance. Furthermore, proposed algorithm can perform better than simple color-based detection and tracking methods because it takes the characteristics of vehicle dynamics into account in image plane. The performance of proposed algorithm is validated by post image processing using video clip taken from flight test and actual flight test with simple avoidance maneuver.

Fault Tolerant Control of Hexacopter for Actuator Faults using Time Delay Control Method

A novel attitude tacking control method using Time Delay Control (TDC) scheme is developed to provide robust controllability of a rigid hexacopter in case of single or multiple rotor faults. When the TDC scheme is developed, the rotor faults such as the abrupt and/or incipient rotor faults are considered as model uncertainties. The kinematics, modeling of rigid dynamics of hexacopter, and design of stability and controllability augmentation system (SCAS) are addressed rigorously in this paper. In order to compare the developed control scheme to a conventional control method, a nonlinear numerical simulation has been performed and the attitude tracking performance has been compared between the two methods considering the single and multiple rotor faults cases. The developed control scheme shows superior stability and robust controllability of a hexacopter that is subjected to one or multiple rotor faults and external disturbance, i.e., wind shear, gust, and turbulence.

Path Tracking Control of a Mobile Robot by Using Dual Estimation Algorithm

Skid-steered mobile robots have been developed to explore unknown environments, especially in rough terrain situations, where the wheel slips always occur and vary when the robot is traveling with different trajectory shapes. The wheel slippage limits the traction and braking abilities of the robot. In this paper, we propose a new dual estimation algorithm to overcome the previous limitations. The estimated values such as the robots positions and wheel slips are obtained based on the Kalman filtering technique. As demonstrated by our experimental results, the advantages of the new method are effective to overcome the slip limitations in the path tracking control problem of a four-track wheel skid-steered mobile robot (4-TW SSMR).

Particle Filter-based Visual Detection of Approaching Aircraft in Complex Background Images

This paper describes a particle-filter based visual processing algorithm that detects approaching aircraft in video feed with complex background images. The algorithm isolates moving aerial objects from homographic image differences of successive frames taken from an onboard camera, and characterizes them as approaching aerial vehicles based on the output of a particle filter with multi-threshold binarization. This algorithm is suitable for low-altitude flight tests as it effectively suppresses noise in complex, dynamically shifting background images, and it is less sensitive than other algorithms to color variations and outdoor sunlight conditions. The performance of the algorithm is validated by applying it to the onboard video feed recorded during a circle-turn flight test using two UAVs.

Gaussian-mixture based potential field approach for UAV collision avoidance

This paper deals with a collision avoidance method for UAVs based on potential field. We derive potential vectors by using a Gaussian distribution function to design an avoidance trajectory and use a Gaussian Mixture Model (GMM) to represent two-dimensional complex shaped obstacles. In addition, we apply the Expectation-Maximization (EM) algorithm to modify the potential field in order to update the information using measured data on the obstacles. For this purpose, after briefly introducing the potential field, we construct a GMM with the EM algorithm and conduct guidance simulations using a point-mass UAV model in order to demonstrate the performance of the algorithm. Finally, we discuss the implications of the current approach and future research direction.