• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.11
• /
• pp.2554-2561
• /
• 2015

In this paper, we strengthen the advantages of a simple PID controller as a study on the formation control of mobile robots and propose an adaptive PID controller with robust performance at the dynamics characteristics of following robot. Simulation studies show that the adaptive PID controller has better keeping constant distance and angle such as tracking performance of following robot for the formation control than a conventional PID controller. This is the proposed adaptive PID controller to change the gains is found to represent the best performance. This is able to verify that the performance of the proposed adaptive PID controller is excellent.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.5
• /
• pp.550-557
• /
• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1668-1672
• /
• 2005

This paper presents the wall following control of a small indoor airship (blimp). The purpose of the wall following control is that a blimp maintains its position and pose and flies along the wall. A blimp has great inertia and it is affected by temperature, atmospheric pressure, disturbance and air flow around blimp. In order to fly indoors, a volume of blimp should be small. The volume of a blimp becomes small then the buoyancy of a blimp should be smaller. Therefore, it is difficult to attach additional equipments on the blimp which are necessary to control blimp. For these reasons, it is difficult to control the pose and position of the blimp during the wall following. In our research, to cope with its defects, we developed new blimp. Generally, a blimp is controlled by using rudders and elevators, however our developed blimp has no rudders and elevators, and it has faster responses than general blimps. Our developed blimp is designed to smoothly follow the wall by using low-cost small ultra sonic sensors instead of high-cost sensors. Finally, the controller is designed to robustly control the pose and position of the blimp which could control in spite of arbitrary disturbance during the wall following, and the effectiveness of the controller is verified by experiment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.12
• /
• pp.1249-1255
• /
• 2004

A new parameter adaptation scheme for RBF Neural Network (NN) has been developed in this paper. Even though the RBF Neural Network (NN) based controllers are robust against both un-modeled dynamics and external disturbances, the performance is not satisfactory for a fast and precise mobile robot. To improve the tracking performance as well as robustness, all the parameters of RBF NN are updated in real time. The stability of this control law is rigorously proved by following the Lyapunov stability theory and shown by the experimental simulations. The fact that all of the weighting factors, width and center of RBF NN have been updated implies that this scheme utilizes all the possibilities in RBF NN to make the controller robust and precise while the mobile robot is following un-known trajectories. The performance of this new algorithm has been compared to the conventional RBF NN controller where some of the parameters are adjusted for robustness.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.415-415
• /
• 2000

The functions of a mobile robot such as obstacle knowledge and collision avoidance for in-door cleaning are necessary features, as has been much studied in the field of industrial automatic guided vehicle or general mobile robot. A mobile robot, in order to avoid collision with obstacles, has to gather data with environment knowledge sensors and recognize environment and the shape of obstacles from the data. In the study, a wall-following algorithm was suggested as a autonomous moving algorithm in which a mobile robot can recognize obstacles in indoor like environment and do cleaning work in effect. The system suggested in the study is for cleaning of nuclear material dusts generated in the process of nuclear fuel manufacturing and decontamination of devices in disorder which is performed in M6 radioactive ray shield hot-cell in IMEF(Irradiated Material Examination Facility) in the Korea Atomic Energy Research Institute.

• The Journal of Korea Robotics Society
• /
• v.6 no.3
• /
• pp.255-262
• /
• 2011

Many intelligent robots have to be given environmental information to perform tasks. In this paper an intelligent robot, that is, a cleaning robot used a sensor fusing method of two sensors: LRF and StarGazer, and then was able to obtain the information. Throughout wall following using laser displacement sensor, LRF, the working area is built during the robot turn one cycle around the area. After the process of wall following, a path planning which is able to execute the work effectively is established using flow network algorithm. This paper describes an algorithm for minimal turning complete coverage path planning for intelligent robots. This algorithm divides the whole working area by cellular decomposition, and then provides the path planning among the cells employing flow networks. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The proposed algorithm is applied to two different working areas, and verified that it is an optimal path planning method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.9
• /
• pp.957-963
• /
• 2014

This paper proposes a predictive control for an efficient human following robot using Kinect sensor. Especially, this research is focused on detecting of foot-end-point and foot-vector instead of human body which can be occluded easily by the obstacles. Recognition of the foot-end-point by the Kinect sensor is reliable since the two feet images can be utilized, which increases the detection possibility of the human motion. Depth image features and a decision tree have been utilized to estimate the foot end-point precisely. A tracking point average algorithm is also adopted in this research to estimate the location of foot accurately. Using the continuous locations of foot, the human motion trajectory is estimated to guide the mobile robot along a smooth path to the human. It is verified through the experiments that detecting foot-end-point is more reliable and efficient than detecting the human body. Finally, the tracking performance of the mobile robot is demonstrated with a human motion along an 'L' shape course.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.3
• /
• pp.287-292
• /
• 2003

The ability of robot is being various and complex. The robot is utilizing distance, image data and voice data for sensing its circumstance. This paper suggests the 2-layer fuzzy control as the algorithm that control robot with various sensor information. In a obstacle avoidance, it utilizes many range finders and classifies them into 3parts(front, left, right). In 3 sub-controllers, the controller executes fuzzy conference. And then it executes combined control with a combination of outputs of 3 sub-controllers in the second step. The text compares the 2-layer fuzzy controller with the hierarchical fuzzy controller that has analogous structure. And the performance of the 2-layer fuzzy controller is confirmed by application this controller to robot following, simulation to each other and real experiment.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.49.1-49
• /
• 2002

$\textbullet$ Intelligent Space 1. Optimal Camera Arrangement 2. People Tracking 3. Physical Robot 4. Robot Control 5. People Following Robot $\textbullet$ Initial stage for making high-level human robot interaction. http://dfs.iis.u-tokyo.ac.jp/∼leejooho/ispace/.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.83.6-83
• /
• 2002

$\textbullet$ Disturbance observer $\textbullet$ Performance enhancement $\textbullet$ LFT formulation $\textbullet$ Structured singular value $\textbullet$ Maximum bandwidth of DOB $\textbullet$ DVD experiments $\textbullet$ Robust stable disturbance observer