• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.36.4-36
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• 2001

A robust and reliable path recognition system is one necessary component for the autonomous navigation of a mobile robot to help determining its current position in its navigation map. This paper describes a computer visual path-recognition system using on-board video camera as vision-based driving assistance for an autonomous navigation mobile robot. The common problem for a visual system is that its reliability was often influenced by different lighting conditions. Here, two different image processing methods for the path detection were developed to reduce the effect of the luminance: one is based on the RGB color model and features of the path, another is based on the HSV color model in the absence of luminance.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.497-502
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• 2015

One of the goals in the field of mobile robotics is the development of personal service robots for the elderly which behave in populated environments. In this paper, we describe a security robot system and ongoing research results that minimize the risk of the elderly and the infirm to access an area to enter restricted areas with high potential for falls, such as stairs, steps, and wet floors. The proposed robot system surveys a potential falling area with an equipped laser scanner sensor. When it detects walking in elderly or infirm patients who in restricted areas, the robot calculates the velocity vector, plans its own path to forestall the patient in order to prevent them from heading to the restricted area and starts to move along the estimated trajectory. The walking human is assumed to be a point-object and projected onto a scanning plane to form a geometrical constraint equation that provides position data of the human based on the kinematics of the mobile robot. While moving, the robot continues these processes in order to adapt to the changing situation. After arriving at an opposite position to the human's walking direction, the robot advises them to change course. The simulation and experimental results of estimating and tracking of the human in the wrong direction with the mobile robot are presented.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.238-246
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• 2013

In this paper, a robot vision technique is presented to detect obstacles, particularly approaching humans, in the images acquired by a mobile robot that autonomously navigates in a narrow building corridor. A single low-cost color camera is attached to the robot, and a trapezoidal area is set as a region of interest (ROI) in front of the robot in the camera image. The lower parts of a human such as feet and legs are first detected in the ROI from their appearances in real time as the distance between the robot and the human becomes smaller. Then, the human detection is confirmed by detecting his/her face within a small search region specified above the part detected in the trapezoidal ROI. To increase the credibility of detection, a final decision about human detection is made when a face is detected in two consecutive image frames. We tested the proposed method using images of various people in corridor scenes, and could get promising results. This method can be used for a vision-guided mobile robot to make a detour for avoiding collision with a human during its indoor navigation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.580-585
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• 2005

A QRT(Quad-Rotor Type) hovering robot system is developed for quick detection and observation of the circumstances under calamity environment such as indoor fire spots. The UAV(Unmanned Aerial Vehicle) is equipped with four propellers driven by each electric motor, an embedded controller using a DSP, INS(Inertial Navigation System) using 3-axis rate gyros, a CCD camera with wireless communication transmitter for observation, and an ultrasonic range sensor for height control. The developed hovering robot shows stable flying performances under the adoption of RIC(Robust Internal-loop Compensator) based disturbance compensation and the vision based localization method. The UAV can also avoid obstacles using eight IR and four ultrasonic range sensors. The VTOL(Vertical Take-Off and Landing) flying object flies into indoor fire spots and sends the images captured by the CCD camera to the operator. This kind of small-sized UAV can be widely used in various calamity observation fields without danger of human beings under harmful environment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.249-254
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• 2001

Abstract In this paper, we propose a method of cooperative control based on artifical intelligent system in distributed autonomous robotic system. In general, multi-agent behavior algorithm is simple and effective for small number of robots. And multi-robot behavior control is a simple reactive navigation strategy by combining repulsion from obstacles with attraction to a goal. However when the number of robot goes on increasing, this becomes difficult to be realized because multi-robot behavior algorithm provide on multiple constraints and goals in mobile robot navigation problems. As the solution of above problem, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for obstacle avoidance. Here, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for their direction to avoid obstacle. Our focus is on system of cooperative autonomous robots in environment with obstacle. For simulation, we divide experiment into two method. One method is motor schema-based formation control in previous and the other method is proposed by this paper. Simulation results are given in an obstacle environment and in an dynamic environment.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.327-335
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• 2021

In this paper, we propose an embedded robot system based on "ROS2 on Yocto" that can support various robots. We developed a lightweight OS based on the Yocto Project as a next-generation robot platform targeting cloud robotics. Yocto Project was adopted for portability and scalability in both software and hardware, and ROS2 was adopted and optimized considering a low specification embedded hardware system. We developed SLAM, navigation, path planning, and motion for the proposed robot system validation. For verification of software packages, we applied it to home cleaning robot and indoor delivery robot that were already commercialized by LG Electronics and verified they can do autonomous driving, obstacle recognition, and avoidance driving. Memory usage and network I/O have been improved by applying the binary launch method based on shell and mmap application as opposed to the conventional Python method. Finally, we verified the possibility of mass production and commercialization of the proposed system through performance evaluation from CPU and memory perspective.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.94-99
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• 1993

In this paper, the method for navigation and obstacle avoidance of an autonomous mobile robot is proposed. It is based on the fuzzy inference system which enables to deal with imprecise and uncertain information, and on the neural network which enables to learn input and output pattern data obtained from ultrasonic sensors. For autonomous navigation, the wall-following navigation utilizing input and output data by an expert's control action is constructed. An approach by the neural network is developed for the obstacle avoidance because of the redundant input data. For an autonomous navigation, the fuzzy control and the control of the neural network are integrated and its feasibility is demonstrated by means of experiment.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.215-221
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• 2010

This paper introduces development of location estimation and navigation system of mobile robots using USN and LEGO Mindstorms NXT. Developed system includes location estimation, location and navigation information display and navigation control parts. It used ZigBee based USN which was built with CC2431 chip to locate blind node and implemented fuzzy model to improve ability of calculation of distances from reference nodes and location of mobile robots. This paper proposed combination method of location estimation using USN and encoder which is built in motors of mobile robots. Experimental results showed proposed method is superior to the method which used USN only in location estimation and navigating robots. Developed system can locate current position of mobile robots and monitor information from sensor nodes like temperature, humidity and send control signal to mobile robot to move.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.2
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• pp.94-102
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• 2012

There have been many homing navigation algorithms for robotic system. In this paper, we suggest a bio-inspired navigation model. It builds path integration based on optical flow. We consider two factors on robot movements, translational movement and rotational movement. For each movement, we found distinguishable optical flows. Based on optical flow, we estimate ego-centric robot movement and integrate the path optimally. We can determine the homing direction and distance. We test this algorithm and evaluate the performance of homing navigation for robotic system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.8
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• pp.471-481
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• 2003

The acts of biological immune system are similar to the navigation for autonomous mobile robots under dynamically changing environments. In recent years, many researchers have studied navigation algorithms using artificial immune networks. Conventional artificial immune algorithms consist of an obstacle-avoidance behavior and a goal-reaching behavior. To select a proper action, the navigation algorithm should combine the obstacle-avoidance behavior with the goal-reaching behavior. In this paper, the neural network is employed to combine the behaviors. The neural network is trained with the surrounding information. the outputs of the neural network are proper combinational weights of the behaviors in real-time. Also, a velocity control algorithm is constructed with the artificial immune network. Through a simulation study and experimental results for a autonomous mobile robot, we have shown the validity of the proposed navigation algorithm.