• Proceedings of the IEEK Conference
• /
• 2000.06e
• /
• pp.23-26
• /
• 2000

Generally, the wheel-driven mobile robot systems, by their structural property, have nonholonomic constraints. These constraints are not integrable and cannot be written as time derivatives of some functions with respect to the generalized coordinates. Hence, nonlinear approaches are required to solve the problems. In this paper, the trajectory controller of wheeled mobile robot systems is suggested to guarantee its convergence to reference trajectory. Design procedure of the suggested trajectory controller is back-stepping scheme which was introduced recently in nonlinear control theory. The performance of the proposed trajectory controller is verified via computer simulation. In the simulation, the trajectory controller is applied to differentially driven robot system and car-like mobile robot system on the assumption that the trajectory planner be given.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.33 no.8
• /
• pp.849-857
• /
• 2009

To guarantee the safety and reliability of obsolete thermal power plants, on site routine patrol in their facilities has been done by human workers. Due to their poor working environments, however, a patrol robot system has been gradually required instead of the human workers from the viewpoint of the workers' safety and work efficiency. For this purpose, this paper presents a patrol robot, controllers, and its control scheme. Especially, this robot system uses a line tracing algorithm, which uses a vision camera instead of IR sensors, and an RFID system for its patrol operation. We confirmed its effectiveness through experiments.

• Proceedings of the KIEE Conference
• /
• 2004.05a
• /
• pp.86-88
• /
• 2004

Personal Robot System in developing, have a module architecture, each module are connected through variety network system like ethernet, WLAN (802.11), IEEE 1394 (firewire), bluetooth, CAN, or RS-232C. In developing personal robot system. We think that the key of robot performance is interoperablity among modules. Each network protocol are well connected in the view of network system for the interoperability. So we make a bridging architecture that can routing converting, and transporting packets with matching each network's properties. Furthermore, we suggest a advanced design scheme for realtime / non-realtime and control signal (short, requiring hard-realtime) / multimedia data (large, requiring soft-realtime).

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.65-68
• /
• 1996

Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. The TMS32OC31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the, network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time, control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.438-443
• /
• 2003

In this paper, we propose a control algorithm for two-wheel mobile robot that can move the rider to his or her command and autonomously keep its balance. The control algorithm is based on a mixed $H_2/H_{\infty}$ control scheme. In this control problem the main issue is to move the rider while keeping its balance in the presence of disturbances and parameter uncertainties. The disturbance force caused by uneven road surfaces and the uncertainty due to different rider's heights are considered. To this end we first consider a state feedback controller as a basic framework. Secondly, we obtain the state feedback gain $K_2$ minimizing the $H_2$ norm and the state feedback gain $K_{\infty}$ minimizing the $H_{\infty}$ norm over the whole range of parameter uncertainty. Finally, we select mixed $H_2$/$H_{\infty}$ state feedback controller K as the geometric mean of $K_2$ and $K_{\infty}$. Simulation results show that the mixed $H_2/H_{\infty}$ state feedback controller combines the effects of the optimal $H_2$ state feedback controller and robust $H_{\infty}$ controller state feedback controller efficiently in the presence of disturbance and parameter uncertainty.

• 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.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.4
• /
• pp.191-202
• /
• 2013

A control structure that makes possible the integration of a kinematic controller and a fuzzy logic (FL) deadzone compensator for mobile robots is presented. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a mobile robot to show its efficacy.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.777-780
• /
• 1991

The practical implementation of model reference adaptive systems(MRAS) using digital computer requires the derivation of discrete-time adaptation laws. This is specially important in the case of direct driver robot and light weight manipulator where inertia changes ang gravity effects are significant. We develope a discrete-time model reference adaptive control scheme for trajectory tracking of robot manipulator. Instead of the conventional Lyapunov approach hyperstabillty theory is more appealing than the Lyapunov approach. It is better suited to discrete time systems and offers more flexibility in design by providing additional free design parameters.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.5
• /
• pp.577-583
• /
• 2007

Simultaneous Localization and Mapping (SLAM) is the process of building a map of an unknown environment and simultaneously localizing a robot relative to this map. SLAM is very important for the indoor navigation of a mobile robot and much research has been conducted on this subject. Although feature-based SLAM using an Extended Kalman Filter (EKF) is widely used, it has shortcomings in that the computational complexity grows in proportion to the square of the number of features. This prohibits EKF-SLAM from operating in real time and makes it unfeasible in large environments where many features exist. This paper presents an algorithm which reduces the computational complexity of EKF-SLAM by using topological information (TI) extracted through a thinning process. The global map can be divided into local areas using the nodes of a thinning-based topological map. SLAM is then performed in local instead of global areas. Experimental results for various environments show that the performance and efficiency of the proposed EKF-SLAM/TI scheme are excellent.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.147-151
• /
• 1991

In this paper a time-optimal path planning scheme for the multiple robot manipulators will be proposed by using hopfield neural network. The time-optimal path planning, which can allow multiple robot system to perform the demanded tasks with a minimum execution time and collision avoidance, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computational burden and thus limits the on-line application. One way to avoid such a difficulty is to rearrange the problem as MTSP(Multiple Travelling Salesmen Problem) and then apply the Hopfield network technique, which can allow the parallel computation, to the minimum time problem. This paper proposes an approach for solving the time-optimal path planning of the multiple robots by using Hopfield neural network. The effectiveness of the proposed method is demonstrated by computer simulation.