Dynamic virtual environment to test teleoperated systems with time delay communications

This paper presents a dynamic virtual environment tool for training operators and to prove different control schemes in telerobotic systems, and describes virtual reality environments used in teleoperated robotic systems. In the presented tool, the kinematic and dynamic model of the remote environment which is manipulating the operator is considered. The paper also describes how time delays in the communication channel can be easily added to the simulator, in order to analyze their effects in the teleoperated system. Finally, some experimental results achieved with this virtual teleoperated system are shown. With the presented dynamic simulator, different control schemes designed to overcome the time delay problem could be tested. © 2005 Wiley Periodicals, Inc.     

Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object

In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory.     

Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator

Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H_?∞? robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system.     

The memorization of in-line sensorimotor invariants: toward behavioral ontogeny and enactive agents

This paper presents a behavioral ontogeny for artificial agents based on the interactive memorization of sensorimotor invariants. The agents are controlled by continuous timed recurrent neural networks (CTRNNs) which bind their sensors and motors within a dynamic system. The behavioral ontogenesis is based on a phylogenetic approach: memorization occurs during the agent’s lifetime and an evolutionary algorithm discovers CTRNN parameters. This shows that sensorimotor invariants can be durably modified through interaction with a guiding agent. After this phase has finished, agents are able to adopt new sensorimotor invariants relative to the environment with no further guidance. We obtained these kinds of behaviors for CTRNNs with 3–6 units, and this paper examines the functioning of those CTRNNs. For instance, they are able to internally simulate guidance when it is externally absent, in line with theories of simulation in neuroscience and the enactive field of cognitive science.     

The Neurally Controlled Animat: Biological Brains Acting with Simulated Bodies

The brain is perhaps the most advanced and robust computation system known. We are creating a method to study how information is processed and encoded in living cultured neuronal networks by interfacing them to a computer-generated animal, the Neurally-Controlled Animat, within a virtual world. Cortical neurons from rats are dissociated and cultured on a surface containing a grid of electrodes (multi-electrode arrays, or MEAs) capable of both recording and stimulating neural activity. Distributed patterns of neural activity are used to control the behavior of the Animat in a simulated environment. The computer acts as its sensory system providing electrical feedback to the network about the Animat's movement within its environment. Changes in the Animat's behavior due to interaction with its surroundings are studied in concert with the biological processes (e.g., neural plasticity) that produced those changes, to understand how information is processed and encoded within a living neural network. Thus, we have created a hybrid real-time processing engine and control system that consists of living, electronic, and simulated components. Eventually this approach may be applied to controlling robotic devices, or lead to better real-time silicon-based information processing and control algorithms that are fault tolerant and can repair themselves.     

Unified reliability estimation and management of NoC based chip multiprocessors

We present a new architecture level unified reliability evaluation methodology for chip multiprocessors (CMPs). The proposed reliability estimation (REST) is based on a Monte Carlo algorithm. What distinguishes REST from the previous work is that both the computational and communication components are considered in a unified manner to compute the reliability of the CMP. We utilize REST tool to develop a new dynamic reliability management (DRM) scheme to address time-dependent dielectric breakdown and negative-bias temperature instability aging mechanisms in network-on-chip (NoC) based CMPs. Designed as a control loop, the proposed DRM scheme uses an effective neural network based reliability estimation module. The neural-network predictor is trained using the REST tool. We investigate how system’s lifetime changes when the NoC as the communication unit of the CMP is considered or not during the reliability evaluation process and find that differences can be as high as 60%. Full-system based simulations using a customized GEM5 simulator show that reliability can be improved by up to 52% using the proposed DRM scheme in a best-effort scenario with 2–9% performance penalty (using a user set target lifetime of 7 years) over the case when no DRM is employed.     

Combining Simulation and Reality in Evolutionary Robotics

Evolutionary Robotics (ER) is a promising methodology, intended for the autonomous development of robots, in which their behaviors are obtained as a consequence of the structural coupling between robot and environment. It is essential that there be a great amount of interaction to generate complex behaviors. Thus, nowadays, it is common to use simulation to speed up the learning process; however simulations are achieved from arbitrary off-line designs, rather than from the result of embodied cognitive processes. According to the reality gap problem, controllers evolved in simulation usually do not allow the same behavior to arise once transferred to the real robot. Some preliminary approaches for combining simulation and reality exist in the ER literature; nonetheless, there is no satisfactory solution available. In this work we discuss recent advances in neuroscience as a motivation for the use of environmentally adapted simulations, which can be achieved through the co-evolution of robot behavior and simulator. We present an algorithm in which only the differences between the behavior fitness obtained in reality versus that obtained in simulations are used as feedback for adapting a simulation. The proposed algorithm is experimentally validated by showing the successful development and continuous transference to reality of two complex low-level behaviors with Sony AIBO¹ robots: gait optimization and ball-kicking behavior. ¹AIBO is a trademark of Sony Corporation     

Embodied metaphors in tangible interaction design

For centuries, learning and development has been supported by physical activity and manipulating physical objects. With the introduction of embedded technologies, opportunities for employing tangible or embodied interaction for learning and development have emerged. As a result of previous research, we have seen that interaction models based on embodied knowledge (through embodied metaphors) can support children’s learning in abstract domains. Although metaphorical mappings are promoted in tangible and embodied interaction research, little is known about how to identify embodied metaphors, or how to implement them effectively into interaction models. In this paper, we introduce a people-centered, iterative approach to the design of tangible learning systems with embodied metaphor-based mappings. As a design case, we implemented our approach to the design of Moving Sounds (MoSo) Tangibles; a tangible system for learning abstract sound concepts. The system consists of a set of interactive tangibles with which children can manipulate pitch, volume, and tempo of ongoing tones. In a user study with 39 participants, we found that all children were able to reproduce sound samples with MoSo Tangibles.     

GP~2N~2S~2系统集成环境与动态图形模拟器的特点与设计

GP~2N~2S~2系统集成环境为用户提供了编辑、编译和运行的共用界面。动态图形模拟器是从属于集成环境,并受之驱动的专用模拟器,它实现了神经网络动态变化过程的模拟。本文讲述了这两个部分的功能特点与设计方法。     

The Effects of Modeling on Simulator Performance

Gate-level simulators are usually thought of in terms of their benefits to logic designers, while behavioral simulators are considered to be the province of system architects. However, the behavioral modeling capabilities of a multilevel gate/behavioral simulator significantly enhanced the performance and accuracy of what are essentially gate-level simulations. The Behave simulator is a multilevel simulator that can simulate circuits at several levels of abstraction?behavioral level, gate level, or a mixture. Zero delay and rank order capability are also available in Behave and can be used to advantage. For example, in a simulation of an array multiplier involving 10,000 vectors, the time decreased from 16 hours to 38 minutes, simply because the elements were rank ordered. This range of processing is possible because of the flexibility in software for general-purpose CPUs.     

Model-based predictive motion cueing strategy for vehicle driving simulators

Motion-based driving simulators are designed to render accelerations perceived as realistic, while keeping the motion system within its physical limits. These objectives are generally contradictory, and consequently motion control strategies are difficult to customize for different simulator configurations. In this paper, a novel approach is presented for the design of motion rendering strategies, using the model-based predictive control theory. Compared to traditional cueing techniques, actuator constraints are always respected, and the use of motion workspace is maximized during simulations. Models of human motion perception can be integrated to reduce sensory conflicts. A practical implementation on a high-performance automotive driving simulator is presented.     

Robust stabilization of the current profile in tokamak plasmas using sliding mode approach in infinite dimension

This paper deals with the robust stabilization of the spatial distribution of tokamak plasmas current profile using a sliding mode feedback control approach. The control design is based on the 1D resistive diffusion equation of the magnetic flux that governs the plasma current profile evolution. The feedback control law is derived in the infinite dimensional setting without spatial discretization. Numerical simulations are provided and the tuning of the controller parameters that would reject uncertain perturbations is discussed. Closed loop simulations performed on realistic test cases using a physics based tokamak integrated simulator confirm the relevance of the proposed control algorithm in view of practical implementation.     

交会对接多航天器联合闭环测试模式设计与验证

在载人航天领域,多航天器交会对接技术是研制的关键和难题,闭环测试系统设计的重要性尤为突出,用于交会对接的多航天器联合闭环测试的设计与实施,实现了多航天器间的一体化实时动态同步电测,实现了多航天器及其测试系统的时序同步,以及敏感器及其模拟器和机电系统的动力学模型同步动态联合实时驱动的同步控制,采用了多航天器一体化实时精确控制的自动化测试,以及多航天器间多通道大回路信息流的实时统一管理。解决飞行任务阶段,多航天器交互状态的验证问题,在地面真实呈现交会对接飞行的全过程,达到多船器联合电测的目的。     

Spheripol工艺均聚PP生产装置全流程仿真培训系统的开发

针对目前世界上领先的Ｓｐｈｅｒｉｐｏｌ均聚ＰＰ生产工艺开发了流程仿真培训系统，介绍了该系统的组成结构，给出了环管反应的简化方法，并建立了反应器的动态模型。     

A dynamic simulator for humanoid robots

Computer simulation is an essential step in the design and construction of various mechanical structures, including biped robots, because it enables rapid testing and virtual prototyping during the construction phase. Although many different simulators are available, this article gives an overview and a motivation for building a new dynamic multibody simulator. The simulator is especially adapted to humanoid robot Archie, developed at the IHRT Institute at the Technical University of Vienna. In addition, it is shown how the simulator can be used not only in the controller design, but also in the online control loop to extend the available sensors: a virtual sensors principle. This work was presented in part at the First European Workshop on Artificial Life and Robtics, Vienna, Austria, July 12–13, 2007     

LPNet: A DNN based latency prediction technique for application mapping in Network-on-Chip design

Analytical models used for latency estimation of Network-on-Chip (NoC) are not producing reliable accuracy. This makes these analytical models difficult to use in optimization of design space exploration. In this paper, we propose a learning based model using deep neural network (DNN) for latency predictions. Input features for DNN model are collected from analytical model as well as from Booksim simulator. Then this DNN model has been adopted in mapping optimization loop for predicting the best mapping of given application and NoC parameters combination. Our simulations show that using the proposed DNN model, prediction error is less than 12% for both synthetic and application specific traffic. More than 108 times speedup could be achieved using DPSO with DNN model compared to DPSO using Booksim simulator.     

基于UML活动图的Web服务合成动态行为建模方法研究

描述了利用UML进行Web服务合成的建模方法,包括静态结构建模和动态行为建模两个方面,针对Web服务合成的动态行为建模部分,详细说明了利用UML活动图进行建模时需要注意的问题,如活动图的控制流模式与Web服务合成控制流模式的语义对应关系,所支持的数据模式,以及为了方便模型转换对活动图actions元素的概念扩展,给出了动态行为建模方法,并给出了基于OCL的转换规则以及UML活动图元素到BPEL4WS元素的映射关系,最后通过订单管理案例对所述方法进行验证,为Web服务合成提供了新的思路。     

Vectorized algorithms for spiking neural network simulation

High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.     

Learning to generate proactive and reactive behavior using a dynamic neural network model with time-varying variance prediction mechanism

This paper discusses a possible neurodynamic mechanism that enables self-organization of two basic behavioral modes, namely a ‘proactive mode’ and a ‘reactive mode,’ and of autonomous switching between these modes depending on the situation. In the proactive mode, actions are generated based on an internal prediction, whereas in the reactive mode actions are generated in response to sensory inputs in unpredictable situations. In order to investigate how these two behavioral modes can be self-organized and how autonomous switching between the two modes can be achieved, we conducted neurorobotics experiments by using our recently developed dynamic neural network model that has a capability to learn to predict time-varying variance of the observable variables. In a set of robot experiments under various conditions, the robot was required to imitate other’s movements consisting of alternating predictable and unpredictable patterns. The experimental results showed that the robot controlled by the neural network model was able to proactively imitate predictable patterns and reactively follow unpredictable patterns by autonomously switching its behavioral modes. Our analysis revealed that variance prediction mechanism can lead to self-organization of these abilities with sufficient robustness and generalization capabilities.