Tunable formation realization for nonholonomic mobile robots using the stress matrix

In this paper, we investigate the formation control problem for a team of nonholonomic mobile robots, in which the overall formation shape needs to be adapted to the change in its surroundings. To cope with the limited accessibility to the desired formation pattern, as well as the inherent nonholonomic constraints, we propose a distributed controller by invoking the stress-matrix-based technique, which gives extra freedom in controlling formations. Theoretical analysis is provided in the context of Lyapunov stability to show that tunable formations can be achieved using our proposed control strategy. Besides, it is also proved that collision avoidance can be ensured in the process of formation deformation. Simulations are carried out to validate the theoretical results

Effects of Hydrogen Plasma on the Electrical Properties of F-Doped ZnO Thin Films and p-i-n -Si:H Thin Film Solar Cells

1.5 wt% zinc fluoride (ZnF2) was mixed with zinc oxide powder to form the F-doped ZnO (FZO) composition. At first, the FZO thin films were deposited at room temperature and 5×10-3 Torr in pure Ar under different deposition power. Hall measurements of the as-deposited FZO thin films were investigated, and then the electrical properties were used to find the deposition power causing the FZO thin films with minimum resistance. The FZO thin films with minimum resistance were further treated by H2 plasma and then found their variations in the electrical properties by Hall measurements. Hydrochloric (HCl) acid solutions with different concentrations (0.1%, 0.2%, and 0.5%) were used to etch the surfaces of the FZO thin films. Finally, the as-deposited, HCl-etched as-deposited, and HCl-etched H2-plasma-treated FZO thin films were used as transparent electrodes to fabricate the p-i-n α-Si:H thin film solar cells and their characteristics were compared in this study. We would show that using H2-plasma-treated and HCl-etched FZO thin films as transparent electrodes would improve the efficiency of the fabricated thin film solar cells

A recurrent emotional CMAC neural network controller for vision-based mobile robots

Vision-based mobile robots often suffer from the difficulties of high nonlinear dynamics and precise positioning requirements, which leads to the development demand of more powerful nonlinear approximation in controlling and monitoring of mobile robots. This paper proposes a recurrent emotional cerebellar model articulation controller (RECMAC) neural network in meeting such demand. In particular, the proposed network integrates a recurrent loop and an emotional learning mechanism into a cerebellar model articulation controller (CMAC), which is implemented as the main component of the controller module of a vision-based mobile robot. Briefly, the controller module consists of a sliding surface, the RECMAC, and a compensator controller. The incorporation of the recurrent structure in a slide model neural network controller ensures the retaining of the previous states of the robot to improve its dynamic mapping ability. The convergence of the proposed system is guaranteed by applying the Lyapunov stability analysis theory. The proposed system was validated and evaluated by both simulation and a practical moving-target tracking task. The experimentation demonstrated that the proposed system outperforms other popular neural network-based control systems, and thus it is superior in approximating highly nonlinear dynamics in controlling vision-based mobile robots

An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots

The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots. In particular, the system inputs are passed into a sensory and emotional channels that jointly produce the final outputs of the network. The non-linear approximation ability of the iFBEL is achieved by taking the BEL network as the emotional channel. The proposed iFBEL works with a robust controller in generating the hand and gait motion of a humanoid robot. The updating rules of the iFBEL-based controller are composed of two parts, including a sensory channel followed by the updating rules of the conventional BEL model, and the updating rules of the FNN and the robust controller which are derived from the "Lyapunov" function. The experiments on a three-joint robot manipulator and a six-joint biped robot demonstrated the superiority of the proposed system in reference to a conventional proportional-integral-derivative controller and a fuzzy cerebellar model articulation controller, based on the more accurate and faster control performance of the proposed iFBEL

Self-organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots

The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel; and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are on-line tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms the proposed network is capable of producing better control performances with high computational efficiency

The Photometric System of Tsinghua-NAOC 80-cm Telescope at NAOC Xinglong Observatory

Tsinghua-NAOC (National Astronomical Observatories of China) Telescope (hereafter, TNT) is an 80-cm Cassegrain reflecting telescope located at Xinglong bservatory of NAOC, with main scientific goals of monitoring various transients in the universe such as supernovae, gamma-ray bursts, novae, variable stars, and active galactic nuclei. We present in this paper a systematic test and analysis of the photometric performance of this telescope. Based on the calibration observations on twelve photometric nights, spanning the period from year 2004 to year 2012, we derived an accurate transformation relationship between the instrumental $ubvri$ magnitudes and standard Johnson $UBV$ and Cousins $RI$ magnitudes. In particular, the color terms and the extinction coefficients of different passbands are well determined. With these data, we also obtained the limiting magnitudes and the photometric precision of TNT. It is worthwhile to point out that the sky background at Xinglong Observatory may become gradually worse over the period from year 2005 to year 2012 (e.g., $\sim$21.4 mag vs. $\sim$20.1 mag in the V band).Comment: 12 pages,9 figures, accepted by RA

Dissolved carbon in a large variety of lakes across five limnetic regions in China

Dissolved carbon in lakes play a vital role in the global carbon cycling. The concentration and dynamics of lake dissolved carbon can be influenced by both the surrounding landscape and a combination of physical, chemical and biological processes within the lakes themselves. From 2009 to 2016, we conducted a large-scale assessment of dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) in 249 lakes across a diverse range of climatic, geopedologic, topographical and hydrological conditions in five Chinese limnetic regions: the East Limnetic Region (ELR), the Northeast Limnetic Region (NLR), the Inner Mongolia-Xinjiang Limnetic Region (MXR), the Yungui Limnetic Region (YGR), and the Tibet-Qinghai Limnetic Region (TQR). We found that the density of the organic matter in the soil in the surrounding landscape plays an important role in the DOC and DIC in lake water, as was evidenced by the high DOC and DIC levels in the NLR, where the soil is respectively organically rich. Conditions in the arid and semi-arid environments (i.e. TQR and MXR) have created a number of brackish/saline lakes and here we found that, DOC and DIC levels (median: 21.79 and 93.72 mg/L, respectively) are significantly higher than those in the freshwater lakes (median: 5.80 and 29.38 mg/L). It also appears to be the case that the trophic state of freshwater lakes influences the spatial variation of DOC. This can be seen in the relationships between DOC and trophic state index (TSI) in agriculturally-dominated regions such as the ELR (R2 = 0.59, p < 0.01), NLR (R2 = 0.65, p < 0.001), and YGR (R2 = 0.78, p < 0.001). Additionally, a close relationship between DOC and DIC can be found in lake waters with different trophic states (eutrophic: slp = 0.63, R2= 0.69; mesotrophic: slp = 1.03, R2 = 0.65; oligotrophic: slp = 1.00, R2 = 0.64). This indicates that human activities influence the quantity and quality of dissolved carbon in inland water across China. This study is able to provide insights regarding the potential effects of climate change and changes in land-use upon the amount of dissolved carbon in lake water

Visual-Guided Robotic Object Grasping Using Dual Neural Network Controllers

It has been a challenging task for a robotic arm to accurately reach and grasp objects, which has drawn much research attention. This article proposes a robotic hand–eye coordination system by simulating the human behavior pattern to achieve a fast and robust reaching ability. This is achieved by two neural-network-based controllers, including a rough reaching movement controller implemented by a pretrained radial basis function for rough reaching movements, and a correction movement controller built from a specifically designed brain emotional nesting network (BENN) for smooth correction movements. In particular, the proposed BENN is designed with high nonlinear mapping ability, with its adaptive laws derived from the Lyapunov stability theorem; from this, the robust tracking performance and accordingly the stability of the proposed control system are guaranteed by the utilization of the H∞ control approach. The proposed BENN is validated and evaluated by a chaos synchronization simulation, and the overall control system by object grasping tasks through a physical robotic arm in a real-world environment. The experimental results demonstrate the superiority of the proposed control system in reference to those with single neural networks