• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.429-434
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• 2013

This paper proposes a CPSO (Cooperative Particle Swarm Optimization)-based MPC (Model Predictive Control) scheme to deal with formation control problem of multiple nonholonomic mobile robots. In a distributed MPC framework, each robot needs to optimize control input sequence over a finite prediction horizon considering control inputs of the other robots where their cost functions are coupled by the state variables of the neighboring robots. In order to optimize the control input sequence, a CPSO algorithm is adopted and modified to fit into the formation control problem. Experiments are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the proposed CPSO-based MPC for multi-robot formation.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.328-338
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• 2006

A new evolutionary computation technique, called particle swarm optimization(PSO), has been proposed and introduced recently. PSO has been inspired by the social behavior of flocking organisms, such as swarms of birds and fish schools and PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. In this paper, characteristics of PSO such as mentioned are reviewed and compared with GA which is based on the evolutionary mechanism in natural selection. Also dimensionalities of PSO and GA are compared throughout numeric experimental studies. The comparative studies demonstrate that PSO is characterized as simple in concept, easy to implement, and computationally efficient and can generate a high-quality solution and stable convergence characteristic than GA.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.2
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• pp.163-172
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• 2007

A swarm-bot docking with two independent robots aiming at overcoming obstacles or climbing up/down stairs is introduced how it can be manufactured and controlled. Utilizing the fast mobility of the vehicle robot and cooperating between robots expands the applications of the robot. An algorithm for identifying the partner robot and its generic mechanism enabling the docking of two robots are addressed. The designed swarm-bot has advantages in terms of overcoming obstacle or stair climbing which is not easily implemented by a single robot, increasing the adaptability to the environment.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.227-232
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• 2010

In this paper we consider the multi-robot system that collects target objects spread in an unexplored environment. The robots cooperate each other to improve the capability and the efficiency. The robots attract or intimidate each other as behaviors of bacterial swarms or particles with electrical moments. The interactions would increase the working efficiency in some environments but it would decrease the efficiency in some other environments. Therefore, the system needs to adapt to the working environment by adjusting the strengths of the interactions. The strengths of the interactions are expressed as sets of gene codes that mean the weights of each kind of attracting or intimidating vectors. The proposed system adjusts the gene codes using evolutional strategy. The proposed approach has been validated by computer simulation. The results of this paper show that our inter-swarm interacting strategy and optimizing algorithm improves the working efficiency, adaptively to the characteristics of environments.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.725-730
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• 2012

In this paper, we propose the localization algorithm for the cooperative behavior of the swarm robots based on WSN (Wireless Sensor Network). The proposed method is as follows: First, we measure positions of the L-bot (Leader robot) and F-bots (Follower robots) by using the APIT (Approximate Point In Triangle) and the RSSI (Received Signal Strength Indication). Second, we measure relative positions of the F-bots against the pre-measured position of the L-bot by using trilateration. Then, to revise a position error caused by noise of the wireless signal, we use the particle filter. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1428-1433
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• 2014

In this paper, we propose the formation control algorithm using the leader-following robots in given space. The proposed method is as follows: First, we plan a path of the leader robot for the obstacle avoidance. After that, we propose the formation control algorithm of the following robots using the position and the orientation angle of the leader robot. Also, we propose method for adjusting the formation of the swarm robots when the following robots detect an obstacles. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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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.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.301-307
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• 2003

A control system design based on coupled nonlinear oscillators (CNOs) for a self- organized swarm system is presented. In this scheme, agents self-organize to flock and arrange group formations through attractive and repulsive forces among themselves using CNOs. Virtual agents are also used to create richer group formation patterns. The objective of the swarm control in this paper is to follow a moving target with a final group formation in the shortest possible time despite some obstacles. The simulation results have shown that the proposed scheme can effectively construct a self-organized multi-agent swarm system capable of group formation and group immigration despite the emergence of obstacles.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2475-2480
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• 2003

A control system design based on coupled nonlinear oscillators (CNOs) for a self-organized swarm system is presented. In this scheme, agents self-organize to flock and arrange group formations through attractive and repulsive forces among themselves using CNOs. Virtual agents are also used to create richer group formation patterns. The objective of the swarm control in this paper is to follow a moving target with a final group formation in the shortest possible time despite some obstacles. The simulation results have shown that the proposed scheme can effectively construct a self-organized multi-agent swarm system capable of group formation and group immigration despite the emergence of obstacles.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.