Modeling,Sensing, and Interpretation of Viscoelastic Contact Interface

Soft robotics is important in the next generation of robots because of the rapidly increasing need for robotics in biomedical applications and the advantages of providing a soft interface for interaction with the physical environment in service robots and other applications. It is indispensable to understand the fundamental behavior of such contact interface, typically viscoelastic, in order to accurately predict the actual elastic and temporal responses of the contact and to successfully control it. Viscoelasticity is a phenomenon of time-dependent strain and/or stress in soft materials. It is therefore important to model such behavior and to study the effects of such time-dependent strain and stress on stability and behavior at the contact interface. The contribution of this paper is the introduction of a novel latency model, which is a nonlinear model with differential equations that govern viscoelastic materials. Latency model describes various features of viscoelastic materials, such as stress relaxation and strain creep. The theoretical modeling was supported by experimental results in which we found two types of relaxation. Type I relaxation is well documented in existing literature but Type II relaxation has not been elaborated previously with the physical insights provided in this paper. The proposed theory can unify both types of time-dependent relaxation responses for modeling, sensing, and interpretation of viscoelastic contact interface.     

Global asymptotic stability analysis for neutral-type complex-valued neural networks with random time-varying delays

This paper investigates the global asymptotic stability problem for a class of neutral-type complex-valued neural networks with random time-varying delays. By introducing a stochastic variable with Bernoulli distribution, the information of time-varying delay is assumed to be random time-varying delays. By constructing an appropriate Lyapunov–Krasovskii functional and employing inequality technique, several sufficient conditions are obtained to ensure the global asymptotically stability of equilibrium point for the considered neural networks. The obtained stability criterion is expressed in terms of complex-valued linear matrix inequalities, which can be simply solved by effective YALMIP toolbox in MATLAB. Finally, three numerical examples are given to demonstrate the efficiency of the proposed main results.     

Distributed adaptive containment control for networked uncertain Euler–Lagrange systems

The paper is concerned with the containment problem of a multi-agent system with each agent being described by an Euler–Lagrange (EL) equation with uncertain parameters. Distributed adaptive control laws are proposed such that the generalised positions of the EL systems converge to the target convex set without measuring or observing neighbours' velocities. Moreover, exponential rate convergence is obtained when the regressor matrix of each EL system is of full column rank. As a byproduct, some of the uncertain parameters existing in the EL systems can be identified under the full column rank condition. Finally, the effectiveness of the proposed method is validated through the simulations on a group of networked uncertain robots.     

Robust reliable control for a near space vehicle with parametric uncertainties and actuator faults

Based on fuzzy control techniques, this article is concerned with the problem of robust reliable control for a near space vehicle (NSV) with parametric uncertainties and actuator faults. The nonlinear dynamics of a NSV is represented by the Takagi–Sugeno fuzzy models, and then the actuator fault model and fuzzy state-space observer are developed. Next, the fuzzy observer-based robust reliable control strategy is proposed. It is proved that the fuzzy control systems for the NSV is reliable in the sense that the closed-loop system is asymptotically stable and the actuator components can operate well in the presence of some actuator faults. The developed theoretical results are in the form of linear matrix inequalities, which can be readily solved via standard numerical software. Finally, simulation results are given to illustrate the applicability of the proposed approach.     

Adaptive detection of range-spread targets without secondary data in multichannel autoregressive process

Adaptive detection of range-spread targets without secondary data is addressed in a multichannel autoregressive Gaussian disturbance with unknown space–time covariance matrix, by utilizing the Rao test. The proposed Rao test without secondary data is theoretically proved to be asymptotically (large-sample in the number of temporal observations) constant false alarm rate with respect to unknown space–time covariance matrix, thanks to an asymptotic equivalence between the Rao test and the generalized likelihood ratio test. Moreover, the performance loss due to no secondary data can be remedied by appropriately increasing the temporal dimension. The performance assessment conducted by Monte Carlo simulation, also in comparison with the existing detector without secondary data, confirms the effectiveness of the proposed detectors.     

An impulse control approach to dike height optimization

This paper determines the optimal timing of dike heightenings as well as the corresponding optimal dike heightenings to protect against floods. To derive the optimal policy, we design an algorithm based on the Impulse Control Maximum Principle. In this way, this paper presents one of the first real-life applications of the Impulse Control Maximum Principle developed by Blaquière. We show that the proposed impulse control (IC) approach performs better than dynamic programming with respect to computational time. This is caused by the fact that IC does not need discretization in time.     

Laser-Based Tracking of Human Position and Orientation Using Parametric Shape Modeling

Robots designed to interact socially with people require reliable estimates of human position and motion. Additional pose data such as body orientation may enable a robot to interact more effectively by providing a basis for inferring contextual social information such as people's intentions and relationships. To this end, we have developed a system for simultaneously tracking the position and body orientation of many people, using a network of laser range finders mounted at torso height. An individual particle filter is used to track the position and velocity of each human, and a parametric shape model representing the person's cross-sectional contour is fit to the observed data at each step. We demonstrate the system's tracking accuracy quantitatively in laboratory trials and we present results from a field experiment observing subjects walking through the lobby of a building. The results show that our method can closely track torso and arm movements, even with noisy and incomplete sensor data, and we present examples of social information observable from this orientation and positioning information that may be useful for social robots.     

Study on Roller-Walker — Improvement of Locomotive Efficiency of Quadruped Robots by Passive Wheels

Abstract

Roller-Walker is a leg–wheel hybrid mobile robot using a passive wheel equipped on the tip of each leg. The passive wheel can be transformed into sole mode by rotating the ankle roll joint when Roller-Walker walks on a rough terrain. This paper discusses the energy efficiency of locomotion in wheeled mode. We define a leg trajectory to produce forward straight propulsion, and discuss the relationships between the parameters of the leg trajectory and energy efficiency of the propulsion using a dynamics simulator. We find optimum parameter sets where optimization criterion is specific resistance. The results indicate that faster locomotion achieves higher energy efficiency. We then carry out hardware experiments and empirically derive the experimental specific resistance. We show that wheeled locomotion has an 8-times higher energy efficiency than the ordinary crawl gait. Finally, we compare the specific resistance of Roller-Walker with other walking robots described in the literature.     

Development and performance evaluation of an improved complex valued radar pulse compressor

Pulse compression is an important and burning issue in radar signal processing. In the recent past, many adaptive and neural network based methods have been proposed to achieve effective pulse compression performance for real coded transmitted waveforms. Even though the radar signal is complex, it is mostly processed as real-valued in-phase and quadrature components. Hence it is desirable that for processing complex radar signal for pulse compression both the structure as well as the learning algorithm associated with it need to be complex in nature. Accordingly in this paper a novel adaptive method is proposed by employing a complex valued fully connected cascaded (CFCC) neural network. For training this network, a new complex Levenberg–Marquardt (CLM) algorithm is derived and used for imparting effective training of its weights. The new CLM based CFCC (CFCC-CLM) model offers superior convergence performance with the least residual mean squared error during training phase compared to those provided by the multilayer perceptron (MLP) trained with complex domain backpropagation (CDBP) and CLM based methods. Further the comparison of peak signal-to-sidelobe ratio (PSR) under noisy and Doppler shift conditions of the proposed method exhibits best performance compared to those offered by the MLP-CDBP, MLP-CLM and the matched filter (MF) based methods.