• Journal of Information Processing Systems
• /
• v.8 no.3
• /
• pp.471-482
• /
• 2012

For disaster exploration and surveillance application, this paper aims to present a novel application of a multi-robot agent based on WSN and to evaluate a multi-hop communication caused by the robotics correspondingly, which are used in the uncertain and unknown subterranean tunnel. A Primary-Scout Multi-Robot System (PS-MRS) was proposed. A chain topology in a subterranean environment was implemented using a trimmed ZigBee2006 protocol stack to build the multi-hop communication network. The ZigBee IC-CC2530 modular circuit was adapted by mounting it on the PS-MRS. A physical experiment based on the strategy of PS-MRS was used in this paper to evaluate the efficiency of multi-hop communication and to realize the delivery of data packets in an unknown and uncertain underground laboratory environment.

• Journal of Power System Engineering
• /
• v.13 no.2
• /
• pp.71-82
• /
• 2009

This paper presents a real-time haptic rendering method for a realistic force feedback in a remote environment with varying communication time-delay. The remote environment is assumed as a virtual environment based on a computer graphics, for example, on-line shopping mall, internet game and cyber-education. The properties of a virtual object such as stiffness and viscosity are assumed to be unknown because they are changed according to the contact position and/or a penetrated depth into the object. The DARMAX model based output estimator is proposed to trace the correct impedance of the virtual object in real-time. The output estimator is developed on the input-output relationship. It can trace the varying impedance in real-time by virtue of P-matrix resetting algorithm. And the estimator can trace the correct impedance by using a white noise that prevents the biased input-output information. Realistic output forces are generated in real-time, by using the inputs and the estimated impedance, even though the communication time delay and the impedance of the virtual object are unknown and changed. The generated forces trace the analytical forces computed from the virtual model of the remote environment. Performance is demonstrated by experiments with a 1-dof haptic device and a spring-damper-based virtual model.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.5
• /
• pp.434-443
• /
• 2007

Presently, the exploration of an unknown environment is an important task for thee new generation of mobile service robots and mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems. This paper presents a technique for localization of a mobile robot using fusion data of multi-ultrasonic sensors and vision system. The mobile robot is designed for operating in a well-structured environment that can be represented by planes, edges, comers and cylinders in the view of structural features. In the case of ultrasonic sensors, these features have the range information in the form of the arc of a circle that is generally named as RCD(Region of Constant Depth). Localization is the continual provision of a knowledge of position which is deduced from it's a priori position estimation. The environment of a robot is modeled into a two dimensional grid map. we defines a vision-based environment recognition, phisically-based sonar sensor model and employs an extended Kalman filter to estimate position of the robot. The performance and simplicity of the approach is demonstrated with the results produced by sets of experiments using a mobile robot.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.22 no.4
• /
• pp.19-25
• /
• 2008

This paper proposes a hierarchically structured navigation algorithm for autonomous mobile robot under unknown environment based on fuzzy-neal network. The proposed algorithm consists of two basic layers as follows. The lower layer consists of two parts such as fuzzy algorithm for goal approach and fuzzy-neural algorithm for obstacle avoidance. The upper layer which is basically fuzzy algorithm adjusts the magnitude of the weighting factor depending on the environmental situation. The proposed algorithm provides an efficient method to escape local mimimum points as shown in the simulation result. Most simulation results show that this algorithm is very effective for autonomous mobile robots' traveling in unknown field.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.9
• /
• pp.1154-1160
• /
• 1999

A rule based controller is constructed for multiple robots accomplishing a given task in unknown environments by using genetic programming. The example task is playing a simplified soccer game, and the controller for robots that governs emergent cooperative behavior is successfully found using the proposed procedure A neural network controller constructed using the rule based controller is shown to be applicable in a more complex environment.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.3
• /
• pp.292-297
• /
• 2016

This paper presents a navigation problem for an autonomous mobile robot in an unknown environment. The environment contains various types of obstacles and is completely unknown to the robot. Therefore, all of the surrounding information must be detected by the robot's proximity sensors. A navigation method was developed based on a fuzzy inference system to guide the robot to move along a collision-free path and reach the goal position quickly. The obstacles are assumed to be static, and both regular and irregular types of obstacles were investigated. A wall following method is also proposed for a special environment that contains a labyrinth or sharp U-valley obstacles. Simulation results demonstrate that the proposed method has great potential for this navigation problem.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.2
• /
• pp.158-163
• /
• 2010

In this paper, we propose the location measurement algorithm of unknown tag based on RFID (Radio-Frequency IDentification) by using RSSI (Received Signal Strength Indication) and TDOA (Time Difference of Arrival) and extended Kalman filter in smart space. To do this, first, we recognize the location of unknown tag by using the RSSI and TDOA recognition methods. Second, we set the coordinate of the tag location measured by using trilateration and SX algorithm. But the tag location data measured by this method are included complex environmental error. So, we use the extended Kalman filter in order to revise error data of the tag location. Finally, we validate the applicability of the proposed method though the simulation in a complex environment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.5
• /
• pp.384-389
• /
• 2003

We propose a hierarchically structured navigation algorithm for multiple mobile robots under unknown dynamic environment based on fussy-neural algorithm. The proposed algorithm consists of two basic layers. The lower layer consists of two parts such as fuzzy algorithm for goal approach and fuzzy-neural algorithm for obstacle avoidance. The upper layer which is basically fuzzy algorithm adjusts the magnitude of the weighting factor depending on the environmental situation. In addition, The proposed algorithm provides an efficient method to escape local mimimum points as shown in the simulation result. The efficacy of the proposed method is demonstrated via some simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.500-503
• /
• 2003

The navigation problem of robot is one of the main themes to deal with conficts or interferences between obstacles and the robot itself In this case, while the robot avoids obstacles on the space, the passage route should be determined efficiently. In order to solve problems above, we have come up with the distance field space medel (DFM) and then, under known environment, we have presented the distance field A algorithm for passage route path search. In this research, the method of performing the 3-dimensional passage route path search of robot under unknown environment is proposed. It is shown that the authors can build the distance search model the does not need space division by taking into account of sensor information to a distance field space model, and constructing this information as virtual obstacle information.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.496-499
• /
• 2003

Support Vector Machines (SVMs) is applied to a practical problem as one of standard tools for machine learning. The application of Reinforcement Learning (RL) and SVMs in action of mobile robot is investigated. A technique to decide the action of autonomous mobile robot in practice is explained in the paper, The proposed method is to find n basis for good action of the system under unknown environment. In multi-dimensional sensor input, the most reasonable action can be automatically decided in each state by RL. Using SVMs, not only optimal decision policy but also generalized state in unknown environment is obtained.