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With the appealing ability of combining both mobility and manipulability, mobile robot manipulators have been applied to factory automation, telemedicine, warehousing, etc. Due to the high density of convenience stores in Taiwan, Asia, and other countries, we make further use of mobile robot manipulators to develop an autonomous replenishment system. The replenishment task for a convenience store poses challenges given its narrow aisles and the occasional presence of customers. It thus demands thorough consideration of the planning, navigation, and control of the robot, in addition to customer safety. Correspondingly, we propose strategies for task and path planning, in addition to control and sensor fusion. Specifically, a new collision-free path planning algorithm (the DWA-PS) that consider both customer safety and comfort is developed, along with replenishment and control strategies that utilize the flexibility of the mobile robot manipulator to raise working efficiency. For the performance evaluation, we apply the proposed system to conduct a series of replenishment experiments, including in a convenience store near our university. [ABSTRACT FROM AUTHOR]

In research on providing motion assistance for elderly care, the robot walking helper is considered to be able to maintain their vitality. For its practicality, one issue of interest is its feasible path planning for guidance. Inspired by the concept of including human factors for path planning previously proposed, in this paper, we develop such a motion guidance system for the robot walking helper. We first selected the human factors most vital for the elderly and also public via an Internet survey, and then developed a corresponding path planning algorithm and control strategy for its realization. Experiments are conducted to demonstrate the effectiveness of the proposed system. Key contributions of the paper lie on (a) one of the few studies that include human factors into path planning of the robot walking helper and (b) a more thorough consideration on comfort with both physical and psychological factors corresponding to elderly and public preference included. [ABSTRACT FROM AUTHOR]

It is well known that complementarity functions play an important role in dealing with complementarity problems. In this paper, we propose a few new classes of complementarity functions for nonlinear complementarity problems and second-order cone complementarity problems. The constructions of such new complementarity functions are based on discrete generalization which is a novel idea in contrast to the continuous generalization of Fischer-Burmeister function. Surprisingly, these new families of complementarity functions possess continuous differentiability even though they are discrete-oriented extensions. This feature enables that some methods like derivative-free algorithm can be employed directly for solving nonlinear complementarity problems and second-order cone complementarity problems. This is a new discovery to the literature and we believe that such new complementarity functions can also be used in many other contexts. [ABSTRACT FROM AUTHOR]

In this paper, we propose two new smooth support vector machines for ε-insensitive regression. According to these two smooth support vector machines, we construct two systems of smooth equations based on two novel families of smoothing functions, from which we seek the solution to ε-support vector regression (ε-SVR). More specifically, using the proposed smoothing functions, we employ the smoothing Newton method to solve the systems of smooth equations. The algorithm is shown to be globally and quadratically convergent without any additional conditions. Numerical comparisons among different values of parameter are also reported. [ABSTRACT FROM AUTHOR]

In response to an aging society, a robot walking helper has come up as a research focus. To be practical for use in daily lives for the elderly, it should be able to follow user intention during maneuver and provide motion assistance when demanded. As quite a number of guidance schemes have been proposed, the studies on maneuver are comparatively few. This paper thus proposes a maneuvering system for the passive type of robot walking helper based on user intention. The proposed system first recognizes motion intention from user-applied force and hip rotation, and then utilizes an assistive control strategy to generate proper braking torque for effective maneuver. Experiments in a real environment are conducted for demonstration. [ABSTRACT FROM PUBLISHER]

With increasing populations of the elderly in our society, robot technology will play an important role in providing functional mobility to humans. From the perspective of human safety, it is desirable that controllers for walk-assist robots be dissipative, i.e., the energy is supplied from the human to the walker, while the controller modulates this energy. The simplest form of a dissipating controller is a brake, where resistive torques are applied to the wheels proportional to their speeds. The fundamental question that we ask in this brief is how to modulate these proportional gains over time for the two wheels so that the walker can perform point-to-point motions. The unique contribution of this brief is a novel way in which the theory of differential flatness is used to plan the trajectory of these braking gains. Since the user input forces are not known a priori , the trajectory of the braking gain is computed iteratively during the motion. Simulation and experimental results show that the walk-assist robot, along with the structure of this proposed control scheme, can guide the user to reach the goal. [ABSTRACT FROM AUTHOR]

In the currently aging society, walk-assist robots can play an important role in improving the activities of daily living of the elderly. In this paper, we propose a robot walking helper with both passive and active control modes for guidance. From the perspective of human safety, the passive mode adopts a braking control law on the wheels to differentially steer the vehicle. However, if the user walks uphill in the outdoor environment, external forces need to be supplied to the human-walker system. In this paper, we add an active mode to guide the user in situations where the passive control mode alone with user-applied forces is not adequate for guidance. The theory of differential flatness is used to plan the trajectory of control gains within the proposed scheme of the controller. Since the user input force and slope angle of the path are not known a priori , the theory of model predictive control is used to periodically compute the trajectory of these control gains. The simulation and experiment results show that the walk-assist robot, along with the structure of this proposed control scheme, can guide the user to a goal on a slope effectively. [ABSTRACT FROM PUBLISHER]

In this paper, we consider using the neural networks to efficiently solve the second-order cone constrained variational inequality (SOCCVI) problem. More specifically, two kinds of neural networks are proposed to deal with the Karush-Kuhn-Tucker (KKT) conditions of the SOCCVI problem. The first neural network uses the Fischer-Burmeister (FB) function to achieve an unconstrained minimization which is a merit function of the Karush-Kuhn-Tucker equation. We show that the merit function is a Lyapunov function and this neural network is asymptotically stable. The second neural network is introduced for solving a projection formulation whose solutions coincide with the KKT triples of SOCCVI problem. Its Lyapunov stability and global convergence are proved under some conditions. Simulations are provided to show effectiveness of the proposed neural networks. [ABSTRACT FROM AUTHOR]