• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.12
• /
• pp.1132-1137
• /
• 2013

In swarm robot systems, it is vital to maintain network connectivity to ensure cooperative behavior between robots. This paper deals with the behavior control algorithm of the swarm robots for maintaining network connectivity. To do this, we divide swarm robots into search-robots, base-robots, and relay-robots. Using these robots, we propose behavior control algorithm to maintain network connectivity. The behavior control algorithms to maintain network connectivity are proposed for the local path planning using virtual force and global path planning using the Delaunay triangulation, respectively. Finally, we demonstrate the effectiveness and applicability of the proposed method through some simulations.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.1
• /
• pp.21-27
• /
• 2012

We can efficiently collect crops or minerals by operating multi-robot foraging. As foraging spaces become wider, control algorithms demand scalability and reliability. Swarm robotics is a state-of-the-art algorithm on wide foraging spaces due to its advantages, such as self-organization, robustness, and flexibility. However, high initial and operating costs are main barriers in performing multi-robot foraging system. In this paper, we propose a novel method to improve the energy efficiency of the system to reduce operating costs. The idea is to employ a new behavior model regarding role division in concert with the search space division.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.9
• /
• pp.812-818
• /
• 2012

In this paper, we propose an asynchronous behavior control algorithm of the swarm robot for surrounding intruders when detected an intruder in a surveillance environment. The proposed method is divided into three parts: First, we proposed the method for the modeling of a state of the swarm robot. Second, we proposed an asynchronous behavior control algorithm for the surrounding an intruder by the swarm robot. Third, we proposed a control method for the collision avoidance with the swarm robot. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.5
• /
• pp.435-441
• /
• 2013

A swarm robot system consists of with multiple mobile robots, each of which is called an agent. Each agent interacts with others and cooperates for a given task and a given environment. For the swarm robotic system, the loss of the entire work capability by malfunction or damage to a single robot is relatively small and replacement and repair of the robot is less costly. So, it is suitable to perform more complex tasks. The essential component for a swarm robotic system is an inter-robot collaboration strategy for teamwork. Recently, the swarm intelligence theory is applied to robotic system domain as a new framework of collective robotic system design. In this paper, FA (Firefly Algorithm) which is based on firefly's reaction to the lights of other fireflies and their social behavior is employed to optimize the group behavior of multiple robots. The main application of the firefly algorithm is performed on path planning of swarm mobile robots and its effectiveness is verified by simulations under various conditions.

• Advances in robotics research
• /
• v.2 no.1
• /
• pp.1-11
• /
• 2018

To monitor the environment and determine the source of a pollutant gradient using a multiple robot swarm, we propose a hybrid algorithm that combines two bio-inspired algorithms mimicking chemotaxis and pheromones of bacteria. The algorithm is implemented in virtual robot agents in a simulator to evaluate their feasibility and efficiency in gradient maps with different sizes. Simulation results show that the chemotaxis controller guided robot agents to the locations with higher pollutant concentrations, while the pheromone marked in a virtual field increased the efficiency of the search by reducing the visiting redundancy. The number of steps required to reach the target point did not increase proportionally as the map size increased, but were less than those in the linear whole-map search method. Furthermore, the robot agents could function with simple sensor composition, minimum information about the map, and low calculation capacity.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.5
• /
• pp.433-438
• /
• 2012

In this paper, a operation network system equipped with onboard wireless communication systems and ground-based mission control systems is proposed for swarm flight of small UAVs. This operating system can be divided into two networks, UAV communication network and ground control system. The UAV communication network is intend to exchange the informations of navigation, mission and flight status with minimum time delay. The ground control system consisted of mission control systems and UDP network. Proposed operation network system can make a swarm flight of various UAVs, execute complex missions decentralizing mission to several UAVs and cooperte several missions. Finally, PILS environments are developed based on the total operating system.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.1
• /
• pp.61-68
• /
• 2010

This paper proposes networked swarm robotic systems with group based control scheme using spring damper impendence feature. The proposed algorithm is applied to keep system arrangement in unexpected situations based on the spring-damper impedance and fuzzy logic. Using the proposed scheme, each robot overcome collision problems efficiently. The structure of UBSR (UMPC Based Swarm Robot) system consists of user level, cognitive level, and executive level. This structure is designed to easily meet the different configuration requirements for other levels. Simulation results show an availability of the proposed method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.5
• /
• pp.465-470
• /
• 2012

This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows: When the environmental condition changes, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.

• Advances in robotics research
• /
• v.2 no.2
• /
• pp.161-182
• /
• 2018

This paper proposes a novel receding horizon control (RHC) algorithm for formation control of a swarm of unmanned surface vehicles (USVs) using particle swarm optimization (PSO). The proposed control algorithm provides the coordinated path tracking of multi-agent USVs while preventing collisions and considering external disturbances such as ocean currents. A three degrees-of-freedom kinematic model of the USV is used for the RHC with guaranteed stability and convergence by incorporating a sequential Monte Carlo (SMC)-based particle initialization. An ocean current model-based estimator is designed to compensate for the effect of ocean currents on the USVs. This method is compared with the PSO-based RHC algorithms to demonstrate the performance of the formation control and the collision avoidance in the presence of ocean currents through numerical simulations.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.5
• /
• pp.416-422
• /
• 2013

This paper suggests a hybrid path planning method of multi-robots, where a path deviation prevention for maintaining a specific formation is implemented by using repulsive function, $A^*$ algorithm and UKF (Unscented Kalman Filter). The repulsive function in potential field method is used to avoid collision among robots and obstacles. $A^*$ algorithm helps the robots to find optimal path. In addition, error estimation based on UKF guarantees small path deviation of each robot during navigation. The simulation results show that the swarm robots with designated formation successfully avoid obstacles and return to the assigned formation effectively.