• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권12호
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• pp.4372-4388
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• 2014

Robotic sensor network (RSN) contains mobile sensors and robots providing feasible solution for many multi-agent applications. One of the most critical issues in RSN and its application is how to effectively assign tasks. This paper presents a novel connectivity preserving hybrid task allocation strategy to answer the question particularly for RSN. Firstly, we model the task allocation in RSN to distinguish the discovering and allocating processes. Secondly, a fully distributed simple Task-oriented Unoccupied Neighbor Algorithm, named TUNA, is developed to allocate tasks with only partial view of the network topology. A connectivity controller is finally developed and integrated into the strategy to guarantee the global connectivity of entire RSN, which is critical to most RSN applications. The correctness, efficiency and scalability of TUNA are proved with both theoretical analysis and experimental simulations. The evaluation results show that TUNA can effectively assign tasks to mobile robots with the requirements of only a few messages and small movements of mobile agents.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.543-545
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• 1998

Robotic soccer is multi-agent system playing soccer game under given rule. This system consists of three mobile robots, vision sensor, action decision module, action execution module and communication module. This paper presents new action decision method using multi-layer neural networks.

• Smart Structures and Systems
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• 제1권4호
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• pp.325-338
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• 2005

Utilizing robotic based reconfigurable nodal structural health monitoring systems has many advantages over static or human positioned sensor systems. However, creating a robot capable of traversing a variety of civil infrastructures is a difficult task, as these structures each have unique features and characteristics posing a variety of challenges to the robot design. This paper outlines the design and implementation of a novel robotic platform for deployment on ferromagnetic structures as an enabling structural health monitoring technology. The key feature of this design is the utilization of an attachment device which is an advancement of the common magnetic base found in the machine tool industry. By mechanizing this switchable magnetic circuit and redesigning it for light weight and compactness, it becomes an extremely efficient and robust means of attachment for use in various robotic and structural health monitoring applications. The ability to engage and disengage the magnet as needed, the very low power required to do so, the variety of applicable geometric configurations, and the ability to hold indefinitely once engaged make this device ideally suited for numerous robotic and distributed sensor network applications. Presented here are examples of the mechanized variable force magnets, as well as a prototype robot which has been successfully deployed on a large construction site. Also presented are other applications and future directions of this technology.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 가을 학술발표논문집 Vol.33 No.2 (D)
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• pp.395-398
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• 2006

최근 Robomote, Robotic Sensor Agents(RSA)와 같은 이동 센서의 등장으로 인해 이동 센서네트워크(MSN: Mobile Sensor Network)에 대한 연구가 활발히 진행되고 있다. 하지만 기존의 이동 센서네트워크에 대한 연구는 주로 기존의 고정 센서네트워크(SSN: Stationary Sensor Network)에서 발생하는 문제점인 coverage hole을 해결하는데 초점을 맞추고 있다. 이러한 연구들에서는 이동 센서들에게 부여된 이동 능력을 최대한 활용하지 못하는 단점을 안고 있다. 이를 해결하기 위해 이동 센서에게 지속적인 이동성을 부여함으로써 고정 센서네트워크에 비해 더 넓은 영역을 센싱하도록 제안한 연구가 있으나, 그 연구가 아직 초기 단계로써 이동 센서의 지속적인 이동으로 인한 싱크 노드로의 통신 경로 설정 및 데이터 전송 문제에 대해서는 논하고 있지 않다. 이에 본 논문에서는 지속적인 이동성을 갖는 이동 센서로 구성된 이동 센서네트워크 환경에서 효율적으로 경로 설정 및 데이터 전송을 가능하게 하는 통신 프로토콜을 제안한다. 제안하는 프로토콜에서는 이동 센서와 함께 고정 센서를 배치함으로써 고정 센서가 이동 센서를 대신하여 싱크 노드로 센싱 데이터를 전송하도록 한다. 시뮬레이션을 이용한 성능 평가를 통해 제안한 통신 프로토콜이 기존의 고정 센서네트워크에 비해 센싱 영역 성능에서 우수함을 보여준다.

• 정보처리학회논문지C
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• 제14C권2호
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• pp.139-146
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• 2007

최근 Robomote, Robotic Sensor Agents(RSAs)와 같은 이동 센서의 등장으로 인해 이동 센서네트워크(MSN: Mobile Sensor Network)에 대한 연구가 활발히 진행되고 있다. 하지만 기존의 이동 센서네트워크에 대한 연구는 주로 기존의 고정 센서네트워크(SSN: Stationary Sensor Network)에서 발생하는 문제점인 Coverage Hole을 해결하는데 초점을 맞추고 있다. 이러한 연구들에서는 이동 센서들에게 부여된 이동 능력을 최대한 활용하지 못하는 단점을 안고 있다. 이를 해결하기 위해 이동 센서에게 지속적인 이동성을 부여함으로써 고정 센서네트워크에 비해 더 넓은 영역을 센싱하도록 제안한 연구가 있으나, 그 연구가 아직 초기 단계로써 이동 센서의 지속적인 이동으로 인한 싱크 노드로의 통신 경로선정 및 데이터 전송 문제에 대해서는 논하고 있지 않다. 이에 본 논문에서는 지속적인 이동성을 갖는 이동 센서로 구성된 이동 센서네트워크 환경에서 효율적으로 경로 설정 및 데이터 전송을 가능하게 하는 통신 프로토콜을 제안한다. 제안하는 프로토콜에서는 이동 센서와 함께 고정센서를 배치함으로써 고정 센서가 이동 센서를 대신하여 싱크 노드로 센싱 데이터를 전송하도록 한다. 시뮬레이션을 이용한 성능 평가를 통해 제안한 통신 프로토콜이 기존의 고정 센서네트워크에 비해 네트워크 커버리지 면에서 최대 40%, 트래픽 오버헤드 부분에서는 최대 76%의 성능을 향상시킴을 보인다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권2호
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• pp.127-134
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• 2004

In this paper, human motor behaving model based sensory motor coordination(SMC) algorithm is implemented on robotic grasping task. Compare to conventional SMC models which connect sensor to motor directly, the proposed method used biologically inspired human behaving system in conjunction with SMC algorithm for fast grasping force control of robot arm. To characterize various grasping objects, pressure sensors on hand gripper were used. Measured sensory data are simultaneously transferred to perceptual mechanism(PM) and long term memory(LTM), and then the sensory information is forwarded to the fastest channel among several information-processing flows in human motor system. In this model, two motor learning routes are proposed. One of the route uses PM and the other uses short term memory(STM) and LTM structure. Through motor learning procedure, successful information is transferred from STM to LTM. Also, LTM data are used for next moor plan as reference information. STM is designed to single layered perception neural network to generate fast motor plan and receive required data which comes from LTM. Experimental results showed that proposed method can control of the grasping force adaptable to various shapes and types of greasing objects, and also it showed quicker grasping-behavior lumining time compare to simple feedback system.

• Advances in robotics research
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• 제1권1호
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• pp.101-126
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• 2014

In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions are achieved by a phase switching network (PSN) module. The combination of these modules generates various locomotion patterns and a reactive obstacle avoidance behavior. The behavior is driven by sensor inputs, to which additional neural preprocessing networks are applied. The complete neural circuitry is developed and tested using a physics simulation environment. This study verifies that the neural modules can serve a general purpose regardless of the robot's specific embodiment. We also believe that our neural modules can be important components for locomotion generation in other complex robotic systems or they can serve as useful modules for other module-based neural control applications.

• Journal of Information Processing Systems
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• 제11권3호
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• pp.370-388
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• 2015

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.