• International Journal of Control, Automation, and Systems
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• 제6권5호
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• pp.722-730
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• 2008

This paper proposes two novel algorithms enabling mobile robots to cooperate with each other in a reliability-based system and a time-critical system. In the reliability-based cooperative system, the concepts of a mobile ad hoc network (MANET) and an object entropy are adopted in order to coordinate a specific task. A logical robot group is created based on the exchange of request and reply messages in a robot communication group whose organization depends on transmission range. In the time-critical cooperative system, relational entropy is used to define the relationship between mobile robots. A group leader is selected based on optimizing power consumption. The proposed algorithm has been verified based on the computer-based simulation and soccer robot experiment. The performance metrics are defined. The metrics include the number of messages needed to make a logical robot group and to obtain the relationship of robots and the power consumption to select a group leader. They are verified by simulation and experiment.

• 로봇학회논문지
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• 제1권1호
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• pp.58-65
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• 2006

The emotional interface is essential technology for the robot to provide the proper service to the user. In this research, we developed emotional components for the service robot such as a neural network based facial expression recognizer, emotion expression technologies based on 3D graphical face expression and joints movements, considering a user's reaction, behavior selection technology for emotion expression. We used our humanoid robots, AMI and AMIET as the test-beds of our emotional interface. We researched on the emotional interaction between a service robot and a user by integrating the developed technologies. Emotional interface technology for the service robot, enhance the performance of friendly interaction to the service robot, to increase the diversity of the service and the value-added of the robot for human. and it elevates the market growth and also contribute to the popularization of the robot. The emotional interface technology can enhance the performance of friendly interaction of the service robot. This technology can also increase the diversity of the service and the value-added of the robot for human. and it can elevate the market growth and also contribute to the popularization of the robot.

• 전자공학회논문지S
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• 제34S권6호
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• pp.1-10
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• 1997

This paper presents an adaptive optimal learning controller for uncertian robot systems which makes use fo simple DNN(dynamic neural network) units to estimate uncertain parameters and learn the unknown desired optimal input. With the aid of a lyapunov function, it is shown that all that error signals in the system are bounded and the robot trajectory converges to the desired one globally exponentially. The effectiveness of the proposed controller is hsown by applying the controller to a 2-DOF robot manipulator.

• 제어로봇시스템학회논문지
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• 제6권12호
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• pp.1079-1085
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• 2000

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• 전기학회논문지
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• 제63권5호
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• pp.683-689
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• 2014

The paper introduces dynamic generation technique of foot trajectories for humanoid robots using CPG(Central Pattern Generator) and proposes adaptive walking method for slope terrains combining a feedback network. The proposed CPG based technique generates the trajectory of foot in the Cartesian coordinates system and it can change the step length adaptively according to the feedback information. To cope with variable slope terrains, the sensory feedback network in the CPG are designed using the geometry relationship between foot position and body center position such that humanoid robot can maintain its stability. To demonstrate the effectiveness of the proposed approach, the experiments on humanoid robot Nao are executed in the Webot simulation. The performance and motion features of the CPG based approach are compared and analyzed focusing on the adaptability in slope terrains.

• 로봇학회논문지
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• 제18권3호
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• pp.352-357
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• 2023

Since robotic assembly in an unstructured environment is very difficult with existing control methods, studies using artificial intelligence such as reinforcement learning have been conducted. However, since long-time operation of a robot for learning in the real environment adversely affects the robot, so a method to shorten the learning time is needed. To this end, a method based on a pre-trained neural network was proposed in this study. This method showed a learning speed about 3 times than the existing methods, and the stability of reward during learning was also increased. Furthermore, it can generate a more optimal policy than not using a pre-trained neural network. Using the proposed reinforcement learning-based assembly trajectory generator, 100 attempts were made to assemble the power connector within a random error of 4.53 mm in width and 3.13 mm in length, resulting in 100 successes.

• Smart Structures and Systems
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• 제8권1호
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• pp.39-52
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• 2011

We are developing a biomimetic robot based on the Sea Lamprey. The robot consists of a cylindrical electronics bay propelled by an undulatory body axis. Shape memory alloy (SMA) actuators generate propagating flexion waves in five undulatory segments of a polyurethane strip. The behavior of the robot is controlled by an electronic nervous system (ENS) composed of networks of discrete-time map-based neurons and synapses that execute on a digital signal processing chip. Motor neuron action potentials gate power transistors that apply current to the SMA actuators. The ENS consists of a set of segmental central pattern generators (CPGs), modulated by layered command and coordinating neuron networks, that integrate input from exteroceptive sensors including a compass, accelerometers, inclinometers and a short baseline sonar array (SBA). The CPGs instantiate the 3-element hemi-segmental network model established from physiological studies. Anterior and posterior propagating pathways between CPGs mediate intersegmental coordination to generate flexion waves for forward and backward swimming. The command network mediates layered exteroceptive reflexes for homing, primary orientation, and impediment compensation. The SBA allows homing on a sonar beacon by indicating deviations in azimuth and inclination. Inclinometers actuate a bending segment between the hull and undulator to allow climb and dive. Accelerometers can distinguish collisions from impediment to allow compensatory reflexes. Modulatory commands mediate speed control and turning. A SBA communications interface is being developed to allow supervised reactive autonomy.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.308-313
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• 2005

For a real-time quadruped robot running control, there are many important objectives to consider. In this paper, the running control architecture based on global states, which describe the cyclic target motion, and central pattern is proposed. The main goal of the controller is how the robot can have robustness to an unpredictable environment with reducing calculation burden to generate control inputs. Additional goal is construction of a single framework controller to avoid discontinuities during transition between multi-framework controllers and of a training-free controller. The global state dependent neuron network induces adaptation ability to an environment and makes the training-free controller. The central pattern based approach makes the controller have a single framework, and calculation burden is resolved by extracting dynamic equations from the control loop. In our approach, the model of the quadruped robot is designed using anatomical information of a cat, and simulated in 3D dynamic environment. The simulation results show the proposed single framework controller is robustly performed in an unpredictable sloped terrain without training.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2834-2836
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• 2000

This paper introduces the behavior-based learning controller for mobile robot using topological map. When the mobile robot navigates to the goal position, it utilizes given information of topological map and its location. Under navigating in unknown environment, the robot classifies its situation using ultrasonic sensor data, and calculates each motor schema multiplied by respective gain for all behaviors, and then takes an action according to the vector sum of all the motor schemas. After an action, the information of the robot's location in given topological map is incorporated to the learning module to adapt the weights of the neural network for gain learning. As a result of simulation, the robot navigates to the goal position successfully after iterative gain learning with topological information.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.181-184
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• 1998

CAM-Brain is a model to develop neural networks based in cellular automata by evolution, and finally aims at a model as and artificial brain,. In order to show the feasibility of evolutionary engineering to develop an artificial brain we have attempted to evolve a module of CAM-Brain for the problem to control a mobile robot, In this paper, we present some recent results obtained by analyzing the behaviors of the evolved neural module. Several experiments reveal a couple of problems that should be solved when CAM-Brain evolves to control a mobile robot. so that some modification of the original model is proposed to solve them. The modified CAM-Brain has evolved to behave well in a simulated environment, and a thorough analysis proves the power of evolution.