• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.9
• /
• pp.898-904
• /
• 2010

Unmanned autonomous forklifts have a great potential to enhance the productivity of material handling in various applications because these forklifts can pick up and deliver loads without an operator and any fixed guide. There are, however, many technical difficulties in developing such forklifts including localization, map building, sensor fusion, control and so on. Implementation, which is often neglected, is one of practical issues in developing such an autonomous device. This is because the system requires numerous sensors, actuators, and controllers that need to be connected with each other, and the number of connections grows very rapidly as the number of devices grows. Another requirement on the integration is that the system should allow changes in the system design so that modification and addition of system components can be accommodated without too much effort. This paper presents a network-based distributed approach where system components are connected to a shared CAN network, and control functions are divided into small tasks that are distributed over a number of microcontrollers with a limited computing capacity. This approach is successfully applied to develop an unmanned forklift.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.2
• /
• pp.282-287
• /
• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.24 no.2
• /
• pp.17-29
• /
• 1999

The manufacturing systems have to cope with the circumstance that the requirements of customers are changed abruptly and the life cycle of product becomes short. In this paper, to develop the efficient control and operation of autonomous, distributed machining systems the concept of Holonic Manufacturing System is adopted and methods for the control and operation of the system are proposed including an evaluation function for the negotiation between holons. And it is applied to scheduling and selection of operations to be worked with consideration of quality. It is expected that the proposed methods may be applied to operate autonomous, distributed machining systems.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.10
• /
• pp.1051-1058
• /
• 2011

Unmanned autonomous forklifts have a great potential to enhance the productivity of material handling in various applications because these forklifts can pick up and deliver loads without an operator and any fixed guide. Especially, automation of pallet loading and unloading technique is useful for enhancing performance of logistics and reducing cost for automation system. There are, however, many technical difficulties in developing such forklifts including localization, map building, sensor fusion, control, and so on. This is because the system requires numerous sensors, actuators, and controllers that need to be connected with each other, and the number of connections grows very rapidly as the number of devices grows. This paper presents a vision sensorbased autonomous loading and unloading for network-based unmanned forklift where system components are connected to a shared CAN network. Functions such as image processing and control algorithm are divided into small tasks that are distributed over a number of microcontrollers with a limited computing capacity. And the experimental results show that proposed architecture can be an appropriate choice for autonomous loading in the unmanned forklift.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.29-31
• /
• 1987

As the conventional manipulators are centralized system, they are considered to have many problems in future because of their wiring and software. For this reason, a rather advanced intelligent manipulator Is not able to realize by using the centralized concept. And this paper describes the manipulator based on autonomous distributed concept to solve the problems.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.11
• /
• pp.1-9
• /
• 2016

Train control systems have changed from wayside electricity-centric to onboard communications-centric. The latest train control system, the CBTC system, has high efficiency for interval control based on two-way radio communications between the onboard and wayside systems. However, since the wayside system is the center of control, the number of input trains to allow a wayside system is limited, and due to the cyclic-path control flows between onboard and wayside systems, headway improvement is limited. In this paper, we propose a train interval-control and train-centric distributed interlocking algorithm for an autonomous train-driving control system. Because an autonomous train-driving control system performs interval and branch control onboard, both tracks and switches are shared resources as well as semaphore elements. The proposed autonomous train-driving control performs train interval control via direct communication between trains or between trains and track-side apparatus, instead of relying on control commands from ground control systems. The proposed interlocking algorithm newly defines the semaphore scheme using a unique key for the shared resource, and a switch that is not accessed at the same time by the interlocking system within each train. The simulated results show the proposed autonomous train-driving control system improves interval control performance, and safe train control is possible with a simplified interlocking algorithm by comparing the proposed train-centric distributed interlocking algorithm and various types of interlock logic performed in existing interlocking systems.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.12
• /
• pp.1079-1085
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.124-127
• /
• 1997

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B lymphocyte(B cell), each environmental condition as an antigen, and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is simulated and suppressed by other robot using communication. Finally much simulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1994.10a
• /
• pp.701-706
• /
• 1994

Holonic manufacturing system is a new approachto the organization and architecture of decentralized, autonomous and cooperative manufacturing system. The new paradigm combines the concepts of hierarchical systems and the integration of autonomous elements in distributed system. Today's scheduling and control techniques are mostly based on a centralized structure. Only little work has been done on scheduling and control of decentralized, autonomous and cooperative manufacturing system. This paper proposes a new approach IPM(Interactive Prediction Method) for scheduling and control of holonic manufacturing system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.7
• /
• pp.591-597
• /
• 2001

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). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in 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. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control school is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.