Widyawardana Adiprawita

Robust MIMO H∞ Integral-Backstepping PID Controller for Hovering Control of Unmanned Model Helicopter

The problem of stabilization of a model helicopter in a hover configuration subject to parametric uncertainty and external disturbances is addressed. Multiinput multioutput (MIMO) proportional-integral-derivative (PID) control law is reformulated into a full-state feedback control law to synthesize the controller by using robust H∞ control theory. In full-state feedback representation, PID control has implicit integral-backstepping structure. Therefore a new parameter, ρ, can be introduced that acts on the derivative of the control signal. The parameters of MIMO PID controller are then obtained with solving the algebraic Riccati equation with selecting the values of ρ and γ. Model helicopter simulation is carried out to verify the performance of the proposed controller to stabilize the uncertain helicopter model and to suppress external disturbances.

Automated Flight Test and System Identification for Rotary Wing Small Aerial Platform using Frequency Responses Analysis

This paper proposes an autopilot system that can be used to control the small scale rotorcraft during the flight test for linear-frequency-domain system identification. The input frequency-sweep is generated automatically as part of the autopilot control command. Therefore the bandwidth coverage and consistency of the frequency-sweep are guaranteed to produce high quality data for system identification. Beside that, we can set the safety parameters during the flight test (maximum roll/pitch value, minimum altitude, etc.) so the safety of the whole flight test is guaranteed. This autopilot system is validated using hardware in the loop simulator for hover flight condition.

UAV path planning using potential field and modified receding horizon A* 3D algorithm

Path planning is an important step to design autonomous flight system of UAV. This paper proposes method for designing path planning algorithm that can make UAV move autonomously. The algorithm used potential field method and A* Algorithm. A* algorithm will be created in a 3D environment, and developed to become hierarchical A* 3D algorithm, and receding horizon A*3D algorithm. These proposed algorithms will be tested in GUI simulation. The potential field and A* algorithm are combined, which the potential field is used for setting up environment, while A* algorithm is used to search the optimal path. Each proposed algorithm has advantages and weaknesses, in terms of time and distance travelled. In time viewpoint, the most optimal method is hierarchical A*3D algorithm, as it requires a short processing time but requires larger distance than the others, whereas the most optimal distance is the normal method of A* 3D, but it takes processing time longest, so that the most appropriate method to be implemented on the UAV is receding horizon A*3D algorithm, both in terms of processing time and distance, still visible to be implemented.

Hardware‐in‐the‐loop simulation for visual target tracking of octorotor UAV

Purpose – The purpose of this paper is to present the development of hardware‐in‐the‐loop simulation (HILS) for visual target tracking of an octorotor unmanned aerial vehicle (UAV) with onboard computer vision.Design/methodology/approach – HILS for visual target tracking of an octorotor UAV is developed by integrating real embedded computer vision hardware and camera to software simulation of the UAV dynamics, flight control and navigation systems run on Simulink. Visualization of the visual target tracking is developed using FlightGear. The computer vision system is used to recognize and track a moving target using feature correlation between captured scene images and object images stored in the database. Features of the captured images are extracted using speed‐up robust feature (SURF) algorithm, and subsequently matched with features extracted from object image using fast library for approximate nearest neighbor (FLANN) algorithm. Kalman filter is applied to predict the position of the moving target on...

Design and implementation of software architecture behavioral-based robot control system using Active Object Computing Model

In this paper, we propose software architecture of behavioural-based robot control system using Active Object Computing Model. Active Object Computing Model is a programming technique that combines the three components, Event Driven Programming paradigm, Event Driven Software Architecture and UML State chart formalism. The scope of this project includes the activities of designing and implementing behavior-based navigation system software, designing and building autonomous mobile robot for software implementation, testing, observing and analyzing the behaviour of autonomous mobile robot which is controlled by behaviour-based navigation system software. The software has been tested on an autonomous mobile robot and run on ARM Cortex M3 processor using mbed NXP LPC1768 microcontroller and successfully demonstrated the behaviour of obstacle avoidance and object Following. Software Architecture is built using Quantum Platform™ from Quantum Leaps.

New resampling algorithm for particle filter localization for mobile robot with 3 ultrasonic sonar sensor

This paper present a particle filter also known as Monte Carlo Localization (MCL) to solve the localization problem presented before. A new resampling mechanism is proposed. This new resampling mechanism enables the particle filter to converge quicker and more robust to kidnaping problem. This particle filter is simulated in MATLAB and also experimented physically using a simple autonomous mobile robot built with Lego Mindstorms NXT with 3 ultrasonic sonar and RWTH Mindstorms NXT Toolbox for MATLAB to connect the robot to MATLAB. The particle filter with the new resampling algorithm can perform very well in the physical experiments.

Modeling and control of unmanned flying robots

The recent decade has witnessed rapid progress in the design and development of autonomous flying robots. The paper closely examines the evolution of modeling and control for such vehicles. Modeling is viewed as an integral part of model-based control synthesis. The first half of the paper is focused on the review of modelling method for unmanned flying robot dynamics. The second half of the paper discusses a wide array of control methodologies that have proposed and reported in the literature. The state of the art of collision avoidance system as well as control for multi flying robots is highlighted.

Color-based segmentation and feature detection for ball and goal post on mobile soccer robot game field

This study presents the real time implementation of object detection and tracking algorithm on mobile soccer robot. Object detection is considered as one of the most important task because ball and goal post are the main component in soccer. The system uses the combination of color-based segmentation and feature detection to detect the color and also the shape feature of the object used in the soccer robot game. The color segmentation uses thresholding method in Hue, Saturation, and Value (HSV) color space to differentiate the ball and goal post color from other objects in the field. Then, morphological operation is applied to the thersholded image to minimize the error. After that, Hough line transform is applied to detect the feature of the goal post. Then, ellipse detection is also applied to find the ball feature. This step is used so the desired object is correctly detected, not other object that have the same color. The final step is to calculate the image moments to determine the centroid of the objects and tracking it. Objects color, feature, and coordinate are obtained from this purposed method. In the implementation, the robot has successfully detect ball, goal post, and its position in a real time manner.

Humanoid robosoccer goal detection using hough transform

Goal posts detection is a critical robot soccer ability which is needed to be accurate, robust and efficient. A goal detection method using Hough transform to get the detailed goal features is presented in this paper. In the beginning, the image preprocessing and Hough transform implementation are described in detail. A new modification on the θ parameter range in Hough transform is explained and applied to speed up the detection process. Line processing algorithm is used to classify the line detected, and then the goal feature extraction method, including the line intersection calculation, is done. Finally, the goal distance from the robot body is estimated using triangle similarity. The experiment is performed on our university humanoid robot with the goal dimension of 225 cm in width and 110 cm in height, in yellow color. The result shows that the goal detection method, including the modification in Hough transform, is able to extract the goal features seen by the robot correctly, with the lowest speed of 5 frames per second. Additionally, the goal distance estimation is accomplished with maximum error of 20 centimeters.

Service oriented architecture in robotic as a platform for cloud robotic (Case study: Human gesture based teleoperation for upper part of humanoid robot)

A service oriented architecture with Robot Operating System is developed for upper part humanoid robot teleoperation with human gesture recognition. This architecture enables the system to distributed in nodes do physically do not change the humanoid weight.