Basic Behavior Acquisition with Multisensor Integration for a Robot System

A robot system generally has several degrees of freedom of motions as well as different kinds of sensors. Basic behaviors are planned based on these sensors' feedback. We take advantage of the Jacobian matrix to describe the differential relations between the sensor feedback and the motor motions. Hence, the relation between two sensors could be formulated by the two respective Jacobian matrices of both sensors to motors. Multisensor integration can, thus, be employed for better performance of the robot system. Experiments of basic behavior acquisition like gazing and posture control of a robot head are conducted. The performances of the two basic behaviors before and after multisensor integration are compared, which demonstrate the performance and robustness of the proposed multisensor integration method.     

ASTM E1989-98 - The New Instrument Control Standard

The new ASTM E1989-98 Laboratory Equipment Control Interface Specification (LECIS) is a robust standard definition of equipment behavior while under remote control. The goal of the standardization effort is to facilitate “plug-and-play” integration of laboratory automation with standard hardware behavior and software interfaces. The LECIS standardizes laboratory equipment behavior and a message passing scheme between the controller and equipment that synchronizes this behavior. Commercial adoption of this new standard is well under way.     

A study of paint adhesion to polymeric substrates

In order to explore the fundamental mechanism of paint adhesion to polymer substrates the surface of polypropylene- ethylene propylene rubber (PP-EPR) blends was modified by flame or plasma treatments. The changes in surface composition and properties were investigated and discussed in light of the results of simple adhesion tests. The topography and surface properties of the PP-EPR samples were studied by employing various surface sensitive techniques. Additionally, the surface properties of the pre-treated PP-EPR were compared with the model polymers poly(methyl methacrylate) (PMMA) and polycarbonate (PC) displaying a poor and an excellent paint adhesion, respectively. Differential scanning calorimetry (DSC) measurements showed that the miscibility of the polymer substrate with paint components was an essential factor for the understanding of the adhesion mechanism. A general model of paint adhesion to polymer surfaces is proposed, where the degree of interdiffusion of the polymer chains of the substrate and paint in the interphase determines the adhesion strength.     

Synthesized articulated behavior using space-temporal on-line principal component analysis

This paper presents a new framework to synthesize humanoid behavior by learning and imitating the behavior of an articulated body using motion capture. The video-based motion capturing method has been developed mainly for analysis of human movement, but is very rarely used to teach or imitate the behavior of an articulated body to a virtual agent in an on-line manner. Using our proposed applications, new behaviors of one agent can be simultaneously analyzed and used to train or imitate another with a novel visual learning methodology. In the on-line learning phase, we propose a new way of synthesizing humanoid behavior based on on-line learning of principal component analysis (PCA) bases of the behavior. Although there are many existing studies which utilize PCA for object/behavior representation, this paper introduces two criteria to determine if the dimension of the subspace is to be expanded or not and applies a Fisher criterion to synthesize new behaviors. The proposed methodology is well-matched to both behavioral training and synthesis, since it is automatically carried out as an on-line long-term learning of humanoid behaviors without the overhead of an expanding learning space. The final outcome of these methodologies is to synthesize multiple humanoid behaviors for the generation of arbitrary behaviors. The experimental results using a humanoid figure and a virtual robot demonstrate the feasibility and merits of this method.     

Capturing a Qualitative Model of Network Performance and Predicting Behavior

This paper describes a method for constructingbehavior models of communication networks. The methodutilizes archived quantitative performance data createdby a network management platform to create a Quantitative/Qualitative (Q²)Dynamic System representation. The Q²representation captures the predominant qualitative(symbolic) states of the network, qualitative inputevents and transitions among the states resulting from these events. Thissymbolic model allows the network manager to understandthe current system behavior, and predict future possiblebehaviors. We evaluated the method on two sets of archive data. The method shows promise foruse in network management, including network monitoring,fault detection, prognostication andavoidance.     

Thixotropy of Systems with Shear Thinning and Plastic Flow Behavior

The definition of the thixotropy is a decrease in viscosity with time in shear and a subsequent recovery of viscosity after the shear deformation is removed.We ...     

Optimum design of co-cured steel–composite tubular single lap joints under axial load

In this paper, an optimum design method for co-cured steel-composite tubular single lap joints under axial load is proposed based on a failure model which incorporates the nonlinear mechanical behavior of the steel adherend and the failure mode of joints such as composite adherend failure and steel adherend failure. The design parameters considered were the test temperature, the stacking sequence of the composite adherends, the thickness ratio of the steel adherend to the composite adherend, and the existence of scarf in the steel adherend. Stress analysis of the cocured steel-composite tubular single lap joints was performed considering the nonlinear mechanical behavior of the steel adherend, and the fabrication residual thermal stress and thermal degradation of the composite adherend. The method developed may be employed in the joining of hybrid composite structures such as golf clubs and automotive composite propeller shafts in which a carbon/epoxy shaft has normally been bonded to a metal shaft with epoxy adhesives.     

Efficient Behavior Learning Based on State Value Estimation of Self and Others

The existing reinforcement learning methods have been seriously suffering from the curse of the dimension problem, especially when they are applied to multiagent dynamic environments. One of the typical examples is a case of RoboCup competitions since other agents and their behavior easily cause state and action space variations. This paper presents a method of modular learning in a multiagent environment by which the learning agent can acquire cooperative behavior with its teammates and competitive behavior against its opponents. The key ideas to resolve the issue are as follows. First, a two-layer hierarchical system with multilearning modules is adopted to reduce the size of the sensor and action spaces. The state space of the top layer consists of the state values from the lower level and the macro actions are used to reduce the size of the physical action space. Second, the state of the other, to what extent it is close to its own goal, is estimated by observation and used as a state variable in the top layer state space to realize the cooperative/competitive behavior. The method is applied to a four (defense team)-on-five (offense team) game task and the learning agent (a passer of the offense team) successfully acquired the teamwork plays (pass and shoot) within much shorter learning time.     

Integration of vision and walking skills together with high-level strategies for quadruped robots to play soccer

Legged robots taking part in real multi-agent activities represent a very innovative challenge. This domain of research requires developments in three main areas. First, without any feedback information from the environment, there is no way for robots to achieve some tasks autonomously. Fortunately, the quadruped 'Sony' prototypes on which all experiments are carried out are equipped with an enhanced vision system; thanks to its CCD camera located in its head, the robot can obtain color images of the scene around it. Extracting relevant information from the images captured is not easy since it must be done onboard in real time. Moreover, image treatment procedures should have high process rates for the robot to react quickly in front of unexpected events. A special vision module composed of three parts has been designed for these purposes. The second point to focus on is the walking ability of the robot. Quadrupeds are designed to move efficiently and rapidly on flat ground. The objective of the walking module is to generate appropriate walking patterns allowing the machine to walk in the desired direction. Walking gaits are produced like reflexes by the robot itself to adapt to the situation. With regard to the design of these gaits, emphasis has been put on increasing speed and mastering transitions. Finally, the machine should be given a minimum of intelligence since it has to manage vision information and its walking gaits by itself. When involved in situations of cooperation or competition or both, like in a soccer game, a high-level supervision task is welcome. This paper presents detailed developments of these three points and describes how they are implemented on a real robot.