H. Jin Kim

Aerial manipulation using a quadrotor with a two DOF robotic arm

This paper presents aerial manipulation using a quadrotor with a two-DOF robot arm. By considering a quadrotor and robot arm as a combined system, the kinematic and dynamic models are developed, and an adaptive sliding mode controller is designed. With the controller, an autonomous flight experiment is conducted including picking up and delivering an object, which requires accurate control of a quadrotor and robot arm. Overall result shows that the proposed approach demonstrates satisfactory performance as a potential platform which can be utilized in various applications such as inspection, manipulation, or transportation in remote places.

Autonomous Exploration in Unknown Urban Environments for Unmanned Aerial Vehicles

§In this paper, we present an autonomous exploration method for unmanned aerial vehicles in unknown urban environment. We address two major aspects of explorationgathering information about the surroundings and avoiding obstacles in the flight path- by building local obstacle maps and solving for confli ct-free trajectory using model predictive control (MPC) framework. For obstacle sensing, an onboard laser scanner is used to detect nearby objects around the vehicle. An MPC algorithm with a cost function that penalizes the proximity to the nearest obstacle replans the fligh t path in real-time. The adjusted trajectory is sent to the position tracking layer in the UAV a vionics. The proposed approach is implemented on Berkeley rotorcraft UAVs and successfully tested in a series of flights in urban obstacle setup.

Autonomous landing of a VTOL UAV on a moving platform using image-based visual servoing

In this paper we describe a vision-based algorithm to control a vertical-takeoff-and-landing unmanned aerial vehicle while tracking and landing on a moving platform. Specifically, we use image-based visual servoing (IBVS) to track the platform in two-dimensional image space and generate a velocity reference command used as the input to an adaptive sliding mode controller. Compared with other vision-based control algorithms that reconstruct a full three-dimensional representation of the target, which requires precise depth estimation, IBVS is computationally cheaper since it is less sensitive to the depth estimation allowing for a faster method to obtain this estimate. To enhance velocity tracking of the sliding mode controller, an adaptive rule is described to account for the ground effect experienced during the maneuver. Finally, the IBVS algorithm integrated with the adaptive sliding mode controller for tracking and landing is validated in an experimental setup using a quadrotor.

Fully Autonomous Vision-Based Net-Recovery Landing System for a Fixed-Wing UAV

This paper presents an autonomous vision-based netrecovery system for small fixed-wing unmanned aerial vehicles (UAVs). A fixed-wing UAV platform is constructed using various avionic sensors, and integrated with a flight control system and a vision system. The ground operation system consists of a vision station and ground control station that provide operation commands and monitor the UAV status. The vision algorithm to detect the recovery net and provide the bearing angle to the guidance algorithm is explained, along with the discussions on the techniques employed to improve the reliability of visual detection. The system identification process and controller are described, which enables to track given waypoints and to approach the detected net under the pursuit guidance law. Experimental results show the autonomous capabilities including take-off, waypoint following, and vision-based net recovery. The proposed technique can be an effective solution to recover fixed-wing UAVs without resorting to a complicated structure such as an instrumented landing system or expensive sensors such as a differential GPS.

Adaptive Image-Based Visual Servoing for an Underactuated Quadrotor System

This paper presents an adaptive image-based visual servoing (IBVS) integratedwith adaptive slidingmode control for a vision-based operation of a quadrotor unmanned aerial vehicle (UAV). For a seamless integration with underactuated quadrotor dynamics, roll and pitch channels are decoupled from the other channels using virtual features. This allows a simple and accurate algorithm for estimating depth information and successful application of the proposed guidance and control algorithm.By employing an adaptive gain in the IBVS controlmethod, the chance of image feature loss is reduced, and performance and stability of the vision-guided UAV control system are improved. The overall setup allows image features to be placed at the desired position in the image plane of a camera mounted on the quadrotor UAV. Stability of the IBVS systemwith the controller is proved using Lyapunov stability analysis. Performance of the overall approach is validated by numerical simulation, vision integrated hardware-inthe-loop simulation, and experiments. The results confirm that the target image is successfully placed at the desired position of the image plane and the quadrotor state variables are properly regulated, showing robustness in the presence of sensor noise, parametric uncertainty, and vibration from motors.

LMI-Based Gain Synthesis for Simple Robust Quadrotor Control

In this paper, we present a method for using linear matrix inequalities (LMIs) to synthesize controller gains for a quadrotor system. The controller is based on approximate feedback linearization and is structured to allow for tuning similar to proportional-integral-derivative (PID) controllers. The synthesis procedure generates suboptimal gains with respect to mixed H2 and H∞ performance cost functions and a pole placement region constraint. The basic procedure is extended to account for dynamic external disturbances, inexact nonlinearity cancellation, multiplicative actuator uncertainty, and saturated integrators in the control loop. The controller is tested in a real-world flight using 10 Hz position updates with 2 cm standard deviation noise to approximate GPS or vision-based control scenarios.

Robust control of ionic polymer?metal composites

Ionic polymer?metal composites (IPMCs) have been considered for various applications due to their light weight, large bending, and low actuation voltage requirements. However, their response can be slow and vary widely, depending on various factors such as fabrication processes, water content, and contact conditions with the electrodes. In order to utilize their capability in various high-performance microelectromechanical systems, controllers need to address this uncertainty and non-repeatability while improving the response speed. In this work, we identified an empirical model for the dynamic relationship between the applied voltage and the IPMC beam deflection, which includes the uncertainties and variations of the response. Then, four types of controller were designed, and their performances were compared: a proportional?integral?derivative (PID) controller with optimized gains using a co-evolutionary algorithm, and three types of robust controller based on , with loop shaping, and ?-synthesis, respectively. Our results show that the robust control techniques can significantly improve the IPMC performance against non-repeatability or parametric uncertainties, in terms of the faster response and lower overshoot than the PID control, using lower actuation voltage.

A FLIGHT CONTROL SYSTEM FOR AERIAL ROBOTS: ALGORITHMS AND EXPERIMENTS

This paper presents a hierarchical flight control system for unmanned aerial vehicles. The proposed system executes high-level mission objectives by progressively substantiating them into machine-level commands. The acquired information from various sensors is propagated back to the higher layers for reactive decision making. Each vehicle is connected via standardized wireless communication protocol for scalable multi-agent coordination. The proposed system has been successfully implemented on a number of small helicopters and validated in various applications. Results from waypoint navigation, a probabilistic pursuit-evasion game and vision-based target tracking demonstrate the potential of the proposed approach toward intelligent flying robots.

Roll-Pitch-Yaw Integrated Robust Autopilot Design for a High Angle-of-Attack Missile

This paper explores the feasibility of roll-pitch-yaw integrated autopilots for a high angle-of-attack missile. Investigation of the aerodynamic characteristics indicates strong cross-coupling effects between the motions in longitudinal and lateral directions. Robust control techniques based on H ∞ synthesis are employed to design roll- pitch-yaw integrated autopilots. The performance of the proposed roll-pitch-yaw integrated controller is tested in high-fidelity nonlinear 5-degree-of-freedom simulations. The proposed controllers are scheduled as a function of total angle of attack in a linear parameter varying framework with proportional navigation guidance laws. The integrated controller demonstrates satisfactory performance that cannot be achieved by the controller designed in a decoupled manner.

Nonsingular Sliding Mode Guidance for Impact Time Control

A guidance problem for impact time control applicable to salvo attacks is considered based on the sliding mode control. To prevent the singularity of the guidance command, a positive continuous nonlinear function of the lead angle is introduced to the guidance command, which makes the Lyapunov stability negative-semidefinite. This issue is also resolved by the additional component of the guidance command, which makes the sliding mode be the only attractor still without the singularity. The capturability analysis is presented regardless of the initial launching conditions of missiles, which can guarantee a wide range of the capture region. The proposed guidance law is easily extended to a nonmaneuvering target using the predicted interception point. Simulation results confirm the effectiveness of the proposed guidance against a nonmaneuvering target as well as a stationary target with absence and presence of measurement noise.