• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.176-180
• /
• 1998

We may gaze at some peculiar scenes of flocking of birds and fishes. This paper demonstrates that multiple agent mobile robots show complex behaviors from efficient and strategic rules. The simulated flock are realized by a distributed behavioral model and each mobile robot decides its own motion as an individual which moves constantly by sensing the dynamic environment.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.4
• /
• pp.824-831
• /
• 2009

This paper proposes an obstacle avoidance algorithm for an autonomous mobile robot. The proposed method based on the circular navigation with probability distribution finds local-paths to avoid collisions. Futhermore, it makes mobile robots to achieve obstacle avoidance and optimal path planning due to the accurate decision of the final goal. Simulation results are included to show the feasibility of the proposed method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.9
• /
• pp.774-782
• /
• 2001

In this paper, we propose a new approach to the autonomous and high-speed indoor navigation of wheeled mobile robots using hybrid system controller. The hierarchical structure of hybrid system presented consists of high-level reasoning process and the low-level motion control process and the environmental interaction. In a discrete event system, the discrete states are defined by the user-defined constraints and the reference motion commands are specified in the abstracted motions. The hybrid control system applied for the nonholonomic mobile robots can combine the motion planning and autonomous navigation with obstacle avoidance in the indoor navigation problem. For the evaluation of the proposed algorithm, the algorithm is implemented to the two-wheel driven mobile robot system. The experimental results show that the hybrid system approach is an effective method for the autonomous navigation in indoor environments.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.8
• /
• pp.56-64
• /
• 1997

In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.11
• /
• pp.29-39
• /
• 2019

As mobile robot applications increase in real life, the need of low cost autonomous driving are gradually increasing. We propose a novel vision-based real-time lane detection and tracking system that supports autonomous driving of mobile robots in constrained tracks which are designed considering indoor driving conditions of mobile robots. Considering the processing of lanes with various shapes and the pre-adjustment of operation parameters, the system structure with multi-operation modes are designed. In parameter tuning mode, thresholds of the color filter is dynamically adjusted based on the geometric property of the lane thickness. And in the unstable input mode of curved tracks and the stable input mode of straight tracks, lane feature pixels are adaptively extracted based on the geometric and temporal characteristics of the lanes and the lane model is fitted using the least-squared method. The track centerline is calculated using lane models and the motion model is simplified and tracked by a linear Kalman filter. In the driving experiments, it was confirmed that even in low-performance robot configurations, real-time processing produces the accurate autonomous driving in the constrained track.

• Journal of the Korean Society of Industry Convergence
• /
• v.18 no.2
• /
• pp.82-89
• /
• 2015

This study proposes a new approach to design and control for autonomous mobile robots. In this paper, we describes a fuzzy logic based visual servoing system for an autonomous mobile robot. An existing system always needs to keep a moving object in overall image. This mes difficult to move the autonomous mobile robot spontaneously. In this paper we first explain an autonomous mobile robot and fuzzy logic system. And then we design a fuzzy logic based visual servoing system. We extract some features of the object from an overall image and then design a fuzzy logic system for controlling the visual servoing system to an exact position. We here introduce a shooting robot that can track an object and hit it. It is illustrated that the proposed system presents a desirable performance by a computer simulation and some experiments.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2784-2786
• /
• 2000

Mobile robots include control modules for autonomous obstacle avoidance and navigation. They are range modules to detect and avoid obstacles. motor control modules to operate two wheels. and encoder modules for localization. There is needed an appropriate controller for each modules. In this paper. a control system. including 18 channels for Sonar sensors. 4 channels for PWM modules. and 4 channels for encoder modules. is proposed using TMS320C32 DSP adopted with CAN. The board communicates with other modules by CAN. so that mobile robots can perform several tasks in real time. So we can realize on autonomous mobile robot with basic functions such as obstacle avoidance by using the developed controller. Especially. this controller has 100 msec scan time for 16 sonar sensors and can detect closer objects comparing with standard sonar sensors.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.1
• /
• pp.33-40
• /
• 2000

In this paper, an integrated method for the path planning and motion control of wheeled mobile robots using a hybrid system model and control is presented. The hybrid model including the continuous dynamics and discrete dynamics with the continuous and discrete state vector is derived for a two wheel driven mobile robot. The architecture of the hybrid control system for real time path planning and following is designed which has the 3-layered hierarchical structure : the discrete event system using the digital automata as the higher process, the continuous state system for the wheel velocity controls as the lower process, and the interface system as the interaction process between the continuous system as the low level and the discrete event system as the high level. The reference motion commands for autonomous navigation are generated by the abstracted motion in the discrete event system. The motion control tasks including the feasible path planning and autonomous motion control with various initial conditions are investigated as the applications by the simulation studies.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.30.2-30
• /
• 2001

As the era of the human friendly robot looms, the intelligent autonomous mobile robots have obtained tremendous interests in recent years. The robots may be service robots for serving human or industrial robots for replacing human. For the coexistance with human, the robots must be able to feel and recognize three dimensional space that human live. In this paper, we propose three dimensional environmental modeling method based on a neural network technique called Growing Neural Gas Network. The purpose of this neural network is to generate a graphical structure which reflects the topology of the input space. Through this method, the robots´ surroundings are autonomously segmented ...

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.2
• /
• pp.124-130
• /
• 2005

Obstacle avoidance of underwater robots based on a modified virtual force field algorithm is proposed in this paper. The VFF(Virtual Force Field) algorithm, which is widely used in the field of mobile robots, is modified for application to the obstacle avoidance of underwater robots. This Modified Virtual Force Field(MVFF) algorithm using the fuzzy lgoc can be used in moving obstacles avoidance. A fuzzy algorithm is devised to handle various situations which can be faced during autonomous navigation of underwater robots. The proposed obstacle avoidance algorithm has ability to handle multiple moving obstacles. Results of simulation show that the proposed algorithm can be efficiently applied to obstacle avoidance of the underwater robots.