Performance of SOR methods on modern vector and scalar processors

The building-cube method (BCM) is a new generation algorithm for CFD simulations. The basic idea of BCM is to simplify the algorithm in all stages of flow computation to achieve large-scale simulations. Calculation of a pressure field using the Successive Over Relaxation (SOR) method consumes most of the total execution time required for BCM. In this paper, effective implementations on modern vector and scalar processors are investigated. NEC SX-9 and Intel Nehalem-EX are the latest vector and scalar processors. Those processors have much higher peak performances than their previous-generation processors. However, their memory bandwidth improvement cannot catch up with the performance improvement of processors. This is the so-called memory wall problem. In our paper, we discuss optimization techniques for implementation of the SOR method based on architectural characteristics of these modern processors, and evaluate their effects on the sustained performances of these processors for BCM.     

Inchworm robot grippers for clothes manipulation

Manipulating deformable objects like clothes, plastic, and paper by a robot is very challenging. This paper focuses on clothes manipulation as an example. A tracing manipulation method is used here to find a corner of the clothes. In this paper, tracing refers to tracing the clothes’ edge, with the robot’s movement based on feedback from sensors. One difficulty during this edge tracing is to make the robot trace smoothly and speedily without dropping the clothes in the process. This is due to the fact that deformable objects are sensitive to contact forces. A solution to this problem is to design a special robot gripper that can trace the clothes without having to worry about the clothes slipping away. In this paper, the development of inchworm-type grippers is proposed. Two sets of grippers inside a robot hand will allow the robot to trace the clothes more freely because there will always be a gripper firmly holding the clothes at any time. A unique tracing method for towel spreading using the inchworm grippers is also discussed. Experimental results have demonstrated the effectiveness of both the proposed grippers and also the algorithm. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

A distributed control scheme for multiple robotic vehicles to make group formations

This paper presents a novel distributed control scheme of multiple robotic vehicles. Each robotic vehicle in this scheme has its own coordinate system, and it senses its relative position and orientation to others, in order to make group formations. Although there exists no supervisor and each robotic vehicle has only relative position feedback from the others in the local area around itself, all the robotic vehicles are stabilized, which we have succeeded in proving mathematically only in the cases where the attractions between the robots are symmetrical. Each robotic vehicle especially has a two-dimensional control input referred to as a “formation vector” and the formation is controllable by the vectors. The validity of this scheme is supported by computer simulations.     

Imitating others by composition of primitive actions: A neuro-dynamic model

This paper introduces a novel neuro-dynamical model that accounts for possible mechanisms of action imitation and learning. It is considered that imitation learning requires at least two classes of generalization. One is generalization over sensory–motor trajectory variances, and the other class is on cognitive level which concerns on more qualitative understanding of compositional actions by own and others which do not necessarily depend on exact trajectories. This paper describes a possible model dealing with these classes of generalization by focusing on the problem of action compositionality. The model was evaluated in the experiments using a small humanoid robot. The robot was trained with a set of different actions concerning object manipulations which can be decomposed into sequences of action primitives. Then the robot was asked to imitate a novel compositional action demonstrated by a human subject which are composed from prior-learned action primitives. The results showed that the novel action can be successfully imitated by decomposing and composing it with the primitives by means of organizing unified intentional representation hosted by mirror neurons even though the trajectory-level appearance is different between the ones of observed and those of self-generated.     

A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations

This paper presents a distributed smooth time-varying feedback control law for coordinating motions of multiple nonholonomic mobile robots of the Hilare-type to capture/enclose a target by making troop formations. This motion coordination is a cooperative behavior for security against invaders in surveillance areas. Each robot in this control law has its own coordinate system and it senses a target/invader, other robots and obstacles, to achieve this cooperative behavior without making any collision. Each robot especially has a two-dimensional control input referred to as a “formation vector” and the formation is controllable by the vectors. The validity of this control law is supported by computer simulations.     

Neural network approach for multicriteria solid transportation problem

In this paper, we propose neural network approach for multicriteria solid transportation problems(STP). First we suggest a neural network architecture to solve single-objective STP according to augmented Lagrange multiplier method. Due to the massive computing unit-neurons and parallel mechanism of neural network approach can solve the large scale problem efficiently and optimal solution can be got. Then we transform the original multicriteria problem into a single objective problem by global criteria method and adopt the neural network approach to solve it. By this way we can get the satisfactory solution of the original problem. The procedure and efficiency of this approach are shown with numerical simulations.     

Neural network technique for fuzzy multiobjective linear programming

Neural Network(NN) is well-known as one of powerful computing tools to solve optimization problems. Due to the massive computing unit-neurons and parallel mechanism of neural network approach we can solve the large-scale problem efficiently and optimal solution can be gotten. In this paper, we intoroduce improvement of the two-phase approach for solving fuzzy multiobjectve linear programming problem with both fuzzy objectives and constraints and we propose a new neural network technique for solving fuzzy multiobjective linear programming problems. The procedure and efficiency of this approach are shown with numerical simulations.     

Development of an Autonomous Quadruped Robot for Robot Entertainment

In this paper, we present Robot Entertainment as a new field of the entertainment industry using autonomous robots. For feasibility studies of Robot Entertainment, we have developed an autonomous quadruped robot, named MUTANT, as a pet-type robot. It has four legs, each of which has three degree-of-freedom, and a head which also has three degree-of-freedom. Micro camera, stereo microphone, touch sensors, and other sensor systems are coupled with newly developed behavior generation system, which has emotion module as its major components, and generates high complex and interactive behaviors. Agent architecture, real-world recognition technologies, software component technology, and some dedicated devices such as Micro Camera Unit, were developed and tested for this purpose. From the lessons learned from the development of MUTANT, we refined the design concept of MUTANT to derive requirements for a general architecture and a set of interfaces of robot systems for entertainment applications. Through these feasibility studies, we consider entertainment applications a significant target at this moment from both scientific and engineering points of view.     

Numerical computation of cross‐coupled algebraic Riccati equations related to H2/H∞ control problem for singularly perturbed systems

In this paper, we present a numerical algorithm to the cross‐coupled algebraic Riccati equations(CARE) related to H₂/H_∞ control problems for singularly perturbed systems (SPS) by means of Newton's method. The resulting algorithm can be widely used to solve Nash game problems and robust control problems because the CARE is solvable even if the quadratic term has an indefinite sign. We prove that the resulting iterative algorithm has the property of the quadratic convergence. Using the solution of the CARE, we construct the high‐order approximate H₂/H_∞ controller. Copyright © 2004 John Wiley & Sons, Ltd.