A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images. The image is filtered and converted into a binary image first. Then the contour of touching grain kernels is extracted and divided into contour segments (CS) with the concave points on it. The next step is to merge the contour segments, which is the main contribution of this work. The distance measurement (DM) and deviation error measurement (DEM) are proposed to test whether the contour segments pertain to the same kernel or not. If they pass the measurement and judgment, they are merged as a new segment. Finally with these newly merged contour segments, the ellipses are fitted as the representative ellipses for touching kernels. To verify the proposed algorithm, six different kinds of Korean grains were tested. Experimental results showed that the proposed method is efficient and accurate for the separation of the touching grain kernels.

A neural network based controller is derived for a mobile manipulator to track the given trajectories in the workspace. The dynamics of the mobile manipulator is assumed to be unknown completely, and is learned on-line by the radial basis function network (RBFN) with weight adaptation rule derived from the Lyapunov function. Generally, a RBFN can be used to properly approximate a nonlinear function. However, there remains some approximation error inevitably in real application. An additional control input to suppress this kind of error source is also used. The proposed algorithm does not need a priori knowledge about the exact system dynamic parameters. Simulation results for a two-link manipulator on a differential-drive mobile platform are presented to show the effectiveness for uncertain system.

This paper presents a mobile gait training system with body weight support (BWS), which relieves the patient of body weight of a prescribed amount by supporting the subject with a harness. The system is based on electrical motors to achieve system mobility according to the subject's motion of walking while it maintains a constant force on the body based on pneumatic actuators. The mobility of the system seems to be important for a correct gait pattern since the subject is able to walk on the ground actually. The system has' a simple architecture to construct, and is practical to the gait rehabilitation.

A new adaptive multiple neural network controller (AMNNC) with a supervisory controller for a class of uncertain nonlinear dynamic systems was developed in this paper. The AMNNC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple neural networks. The weighted sum of the multiple neural networks was used to approximate system nonlinearity for the given task. Each neural network represents the system dynamics for each task. For a job where some tasks are repeated but information on the load is not defined and unknown or varying, the proposed controller is effective because of its capability to memorize control skill for each task with each neural network. For a new task, most similar existing control skills may be used as a starting point of adaptation. With the help of a supervisory controller, the resulting closed-loop system is globally stable in the sense that all signals involved are uniformly bounded. Simulation results on a cartpole system for the changing mass of the pole were illustrated to show the effectiveness of the proposed control scheme for the comparison with the conventional adaptive neural network controller (ANNC).

A new adaptive controller based on multiple neural networks for uncertain robot manipulator system was developed in this paper. The proposed multiple neuro-adaptive controller (MNAC) switches to a memorized control skill or blends multiple skills by using visual information on the given job to improve transient response at the time of task variation like change of manipulating object. MNAC is a kind of adaptive feedback controller where system nonlinearity terms are approximated with multiple neural networks. The proposed controller is effective for a job where some tasks are repeated but information on the load cannot be scheduled before the operation. During learning phase, MNAC memorizes a control skill for each load with each neural network. For a new task, most similar existing control skill may be used as a starting point of adaptation, which improves the performance of learning. Lyapunov function based design of MNAC guarantees the stability of the closed-loop system to be independent of switching or blending law. Simulation results on two-link manipulator or changing mass of the given load were illustrated to show the effectiveness of the proposed control scheme by the comparison with the conventional neuro-adaptive controller (NAC).

Previous studies of evolutionary algorithms (EA) focus on stationary environments where the evaluation function and constraints of the problem are fixed over time of evolution. This study introduces a new algorithm for optimization in nonstationary environments. In this study, we explore the use of a hierarchically distributed evolution model which enables us to utilize fast local search operators while maintaining the sufficient diversity to adapt the environmental changes. The results are compared with previous techniques with several test functions.

A power assisted gait rehabilitation robot system by which a patient who has difficulty in walking can exercise is proposed. Our developed system consists of a robotic manipulator which can adjust the height of the system according to the user and a mobile base with two-driving wheels. It is similar to the architecture of a mobile manipulator. The neural network based control algorithm for a mobile manipulator used to compensate for dynamic interactions, unmodeled dynamics, and disturbances by the user on the system, is derived and experimental results are shown.

In this paper, a robust fault-tolerant control scheme including fault detection, identification and accommodation for robot manipulators is presented to overcome the actuator failures. The joint fault considered in this paper is the free-swinging joint failure that causes the loss of torque on a joint. The presented fault-tolerant Cartesian control framework includes a normal control with normal (nonfailed) operation, a fault detection and a fault recovery control to achieve task completion and fault accommodation. After the detection and identification of joint failures, the robot manipulator with failed actuators can be considered as the underactuated robot manipulator. To show the feasibility of the proposed fault-tolerant control scheme, simulation results for a three-link planar robot arm are presented.