• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.52-57
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• 2014

This paper presents an implementation of a smooth path planning method considering physical limits on a real time operating system for a two-wheel mobile robot. A Bezier curve is utilized to make a smooth path considering a robot's position and direction angle through the defined path. A convolution operator is used to generate the center velocity trajectory to travel the distance of the planned path while satisfying the physical limits. The joint space velocity is computed to drive the two-wheel mobile robot from the center velocity. Trajectory planning, velocity command according to the planned trajectory, and monitoring of encoder data are implemented with a multi-tasking system. And the synchronization of tasks is performed with a real-time mechanism of Event Flag. A real time system with multi-tasks is implemented and the result is compared with a non-real-time system in terms of path tracking to the designed path. The result shows the usefulness of a real-time multi-tasking system to the control system which requires real-time features.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.712-717
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• 2008

In swarm robot systems, each robot must act by itself according to the its states and environments, and if necessary, must cooperate with other robots in order to carry 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, reinforcement learning method with SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. 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 performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of SVM is adopted in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.279-284
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• 2009

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry 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, reinforcement learning method using many SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. 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 performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of Cascade SVM is adopted in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.463-470
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• 2008

In this paper, we confirm that the two properties, 'existence of obstacles' and 'connectivity between obstacles', involved in information acquired by a robot can be used efficiently for location recognition of the robot by using rough sets. Moreover, we propose a method which can reduce ambiguity of the location recognition by applying the properties and recognize the robot's location with distrustful information of the environment where the robot moves. We confirmed it through computer simulation that a robot moves to a goal with only the map containing not enough information on the real environment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.601-606
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• 1998

In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. Futhermore proper fuzzy partitioning is not deterministic ad there is no unique solution. So we propose a co-evolutionary method finding optimal fuzzy rules and proper fuzzy membership functions at the same time. Predator-Prey co-evolution and symbiotic co-evolution algorithms, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. Our algorithm is that after constructing two population groups made up of rule base and membership function, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the propose method to a path planning problem of autonomous mobile robots when moving objects applying the proposed method to a pa h planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1593-1597
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• 2003

The home service robot supports human beings by performing various kinds of works at home. This paper presents a simple control method for opening a door from the viewpoint of the mobile manipulation. The simulation shows various results of path planning and motion planning for opening a door. The joint trajectories were generated by the simulation system. In general, a six-axis force/torque sensor at an end-effector is needed in order to maintain the static equilibrium of the manipulator. But we show another method. From three components of applied forces which was directly obtained by the three-axis force sensor and three components of applied forces which was indirectly estimated by the joint-torque sensors, all of joint torques that will exactly balance forces at the end-effector in the static situation can be found. It is more practical method than using a six-axis force sensor in a wrist. Experimental results have shown that the opening a door can be realized more effectively from the suggested control method of mobile manipulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.367-372
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• 2001

This paper proposes a hierachically structured navigation algorithm for multiple mobile robots under unknown dynamic environment. The proposed algorithm consists of three basic parts as follows. The first part based on the fuzzy rule generates the turning angle and moving distance of the robot for goal approach without obstacles. In the second part, using both fuzzy and neural network, the angle and distance of the robot to avoid collision with dynamic and static obstacles are obtained. The final adjustment of the weighting factor based on fuzzy rule for moving and avoiding distance of the robots is provided in the third stage. Some simulation results show the effectiveness of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.10
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• pp.983-989
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• 2007

Most of life thing including human being have tendency of reaction with inherently their own pattern against environmental change caused by such as light, sound, smell etc. Especially, a sense of direction often works as a very important factor in such reaction. Actually, human or animal lift that can react instantly to a stimulus determine their action with a sense of direction to a stimulant. In this paper, we try to propose how to give a sense of direction to a robot using sound being representative stimulant, and tracking sensors being able to detect the direction of such sound source. We also try to propose how to determine the relative directions among devices or robots using the digital compass and the RSSI on wireless network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.411-414
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• 2003

Particle swarm optimization (PSO) is employed to train fuzzy-neural networks (FNN), which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. The system has been successfully employed in a real life situation for navigation of a mobile robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.187-192
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• 2008

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human Jollowing by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.