3D Path Planning and Obstacle Avoidance Algorithms for Obstacle-Overcoming Robots

This article introduces a multimodal motion planning (MMP) algorithm that combines three-dimensional (3-D) path planning and a DWA obstacle avoidance algorithm. The algorithms aim to plan the path and motion of obstacle-overcoming robots in complex unstructured scenes. A novel A-star algorithm is proposed to combine the characteristics of unstructured scenes and a strategy to switch it into a greedy best-first strategy algorithm. Meanwhile, the algorithm of path planning is integrated with the DWA algorithm so that the robot can perform local dynamic obstacle avoidance during the movement along the global planned path. Furthermore, when the proposed global path planning algorithm combines with the local obstacle avoidance algorithm, the robot can correct the path after obstacle avoidance and obstacle overcoming. The simulation experiments in a factory with several complex environments verified the feasibility and robustness of the algorithms. The algorithms can quickly generate a reasonable 3-D path for obstacle-overcoming robots and perform reliable local obstacle avoidance under the premise of considering the characteristics of the scene and motion obstacles.Comment: 2nd IEEE International Conference on Electronic Communications, Internet of Things and Big Data Conference 2022 (IEEE ICEIB 2022

Exploring Variability of Trichodesmium Photophysiology Using Multi-Excitation Wavelength Fast Repetition Rate Fluorometry

Fast repetition rate fluorometry (FRRf) allows for rapid non-destructive assessment of phytoplankton photophysiology in situ yet has rarely been applied to Trichodesmium. This gap reflects long-standing concerns that Trichodesmium (and other cyanobacteria) contain pigments that are less effective at absorbing blue light which is often used as the sole excitation source in FRR fluorometers—potentially leading to underestimation of key fluorescence parameters. In this study, we use a multi-excitation FRR fluorometer (equipped with blue, green, and orange LEDs) to investigate photophysiological variability in Trichodesmium assemblages from two sites. Using a multi-LED measurement protocol (447+519+634 nm combined), we assessed maximum photochemical efficiency (Fv/Fm), functional absorption cross section of PSII (σPSII), and electron transport rates (ETRs) for Trichodesmium assemblages in both the Northwest Pacific (NWP) and North Indian Ocean in the vicinity of Sri Lanka (NIO-SL). Evaluating fluorometer performance, we showed that use of a multi-LED measuring protocol yields a significant increase of Fv/Fm for Trichodesmium compared to blue-only excitation. We found distinct photophysiological differences for Trichodesmium at both locations with higher average Fv/Fm as well as lower σPSII and non-photochemical quenching (NPQNSV) observed in the NWP compared to the NIO-SL (Kruskal–Wallis t-test df = 1, p < 0.05). Fluorescence light response curves (FLCs) further revealed differences in ETR response with a lower initial slope (αETR) and higher maximum electron turnover rate ((Formula presented.)) observed for Trichodesmium in the NWP compared to the NIO-SL, translating to a higher averaged light saturation EK (= (Formula presented.) /αETR) for cells at this location. Spatial variations in physiological parameters were both observed between and within regions, likely linked to nutrient supply and physiological stress. Finally, we applied an algorithm to estimate primary productivity of Trichodesmium using FRRf-derived fluorescence parameters, yielding an estimated carbon-fixation rate ranging from 7.8 to 21.1 mgC mg Chl-a–1 h–1 across this dataset. Overall, our findings demonstrate that capacity of multi-excitation FRRf to advance the application of Chl-a fluorescence techniques in phytoplankton assemblages dominated by cyanobacteria and reveals novel insight into environmental regulation of photoacclimation in natural Trichodesmium population

Raffinose degradation-related gene GhAGAL3 was screened out responding to salinity stress through expression patterns of GhAGALs family genes

A-galactosidases (AGALs), the oligosaccharide (RFO) catabolic genes of the raffinose family, play crucial roles in plant growth and development and in adversity stress. They can break down the non-reducing terminal galactose residues of glycolipids and sugar chains. In this study, the whole genome of AGALs was analyzed. Bioinformatics analysis was conducted to analyze members of the AGAL family in Gossypium hirsutum, Gossypium arboreum, Gossypium barbadense, and Gossypium raimondii. Meanwhile, RT-qPCR was carried out to analyze the expression patterns of AGAL family members in different tissues of terrestrial cotton. It was found that a series of environmental factors stimulated the expression of the GhAGAL3 gene. The function of GhAGAL3 was verified through virus-induced gene silencing (VIGS). As a result, GhAGAL3 gene silencing resulted in milder wilting of seedlings than the controls, and a significant increase in the raffinose content in cotton, indicating that GhAGAL3 responded to NaCl stress. The increase in raffinose content improved the tolerance of cotton. Findings in this study lay an important foundation for further research on the role of the GhAGAL3 gene family in the molecular mechanism of abiotic stress resistance in cotton

Upper ocean biogeochemistry of the oligotrophic North Pacific Subtropical Gyre : from nutrient sources to carbon export

Subtropical gyres cover 26–29% of the world’s surface ocean and are conventionally regarded as ocean deserts due to their permanent stratification, depleted surface nutrients, and low biological productivity. Despite tremendous advances over the past three decades, particularly through the Hawaii Ocean Time-series and the Bermuda Atlantic Time-series Study, which have revolutionized our understanding of the biogeochemistry in oligotrophic marine ecosystems, the gyres remain understudied. We review current understanding of upper ocean biogeochemistry in the North Pacific Subtropical Gyre, considering other subtropical gyres for comparison. We focus our synthesis on spatial variability, which shows larger than expected dynamic ranges of properties such as nutrient concentrations, rates of N2 fixation, and biological production. This review provides new insights into how nutrient sources drive community structure and export in upper subtropical gyres. We examine the euphotic zone in subtropical gyres as a two-layered vertically structured system: a nutrient-depleted layer above the top of the nutricline in the well-lit upper ocean and a nutrient-replete layer below in the dimly lit waters. These layers vary in nutrient supply and stoichiometries and physical forcing, promoting differences in community structure and food webs, with direct impacts on the magnitude and composition of export production. We evaluate long-term variations in key biogeochemical parameters in both of these euphotic zone layers. Finally, we identify major knowledge gaps and research challenges in these vast and unique systems that offer opportunities for future studies

Initial public offerings in the disclosure based regime.

This study examines the effects of the shift in the philosophy of regulation for the securities market in Singapore has on the corporate governance structure and level of disclosure of firms launching their Initial Public Offerings. In addition, this paper looks into the effects of corporate governance and level of disclosure on the cost of equity for such firms

Influence of Blasting Disturbance on the Dynamic Stress Distribution and Fracture Area of Rock Tunnels

In order to study the dynamic stress distribution and the fracture area of rock around the tunnel under different orientations of blasting disturbance, AUTODYN finite difference method software was used to conduct numerical simulation research. Gauge monitoring points were set around the numerical model of the tunnel to conduct real-time monitoring of the stress distribution, displacement and fracture area of the tunnel. Based on the analysis of the stress wave propagation law, the following conclusions are obtained: (1) under the condition of the same blasting loads, the stress and displacement of the tunnel is relatively small when the blasting disturbance source is located above the roof, i.e., the stress state of the tunnel is relatively stable and the fracture area around the tunnel is minimal; (2) from the uniaxial stress around the tunnel and the tunnel peripheral displacement, it can be seen that the displacement caused by horizontal direction stress of the tunnel is the largest, and the deformation is mainly concentrated above the floor and at the shoulder, while the vertical wall part has almost no deformation; (3) for brittle materials such as rock, the arch-shaped stress-bearing surface is more likely to disperse stress, while the straight wall and flat floor of the tunnel cannot well disperse stress, resulting in uneven stress on the stress-bearing surface, uncoordinated deformation and ultimately, failure

Practical Obstacle-Overcoming Robot with a Heterogeneous Sensing System: Design and Experiments

It is challenging for robots to improve their ability to pass through unstructured environments while maximizing motion performance in cities and factories. This paper presents an omnidirectional deformable wheeled robot based on a heterogeneous sensing system. We presented a novel structure with dual swing arms and six wheels. Moreover, the heterogeneous sensing system can perceive critical environmental data, such as friction and temperature, to assist the robot in executing different functions. In addition, a top-down ‘Order–Decision–Behaviour’ overall motion strategy is proposed based on the data acquisition. The strategy combines the key condition parameters with a kinetic model to integrate the robot’s movement, overcoming of obstacles, and mode switching. The robot is flexible and fast in moving mode and can overcome obstacles safely, reliably, and simply. This study describes the robot’s design, strategy, simulation, and experiments. Motion performance and strategy were investigated and evaluated in field environments

Contract lenses: Reasoning about bidirectional programs via calculation

Bidirectional transformations (BXs) are a mechanism for maintaining consistency between multiple representations of related data. The lens framework, which usually constructs BXs from lens combinators, has become the mainstream approach to BX programming because of its modularity and correctness by construction. However, the involved bidirectional behaviors of lenses make the equational reasoning and optimization of them much harder than unidirectional programs. We propose a novel approach to deriving efficient lenses from clear specifications via program calculation, a correct-by-construction approach to reasoning about functional programs by algebraic laws. To support bidirectional program calculation, we propose contract lenses, which extend conventional lenses with a pair of predicates to enable safe and modular composition of partial lenses. We define several contract-lens combinators capturing common computation patterns including fold, filter, map, and scan, and develop several bidirectional calculation laws to reason about and optimize contract lenses. We demonstrate the effectiveness of our new calculation framework based on contract lenses with nontrivial examples.<br/

Contract lenses: Reasoning about bidirectional programs via calculation

Bidirectional transformations (BXs) are a mechanism for maintaining consistency between multiple representations of related data. The lens framework, which usually constructs BXs from lens combinators, has become the mainstream approach to BX programming because of its modularity and correctness by construction. However, the involved bidirectional behaviors of lenses make the equational reasoning and optimization of them much harder than unidirectional programs. We propose a novel approach to deriving efficient lenses from clear specifications via program calculation, a correct-by-construction approach to reasoning about functional programs by algebraic laws. To support bidirectional program calculation, we propose contract lenses, which extend conventional lenses with a pair of predicates to enable safe and modular composition of partial lenses. We define several contract-lens combinators capturing common computation patterns including fold, filter, map, and scan, and develop several bidirectional calculation laws to reason about and optimize contract lenses. We demonstrate the effectiveness of our new calculation framework based on contract lenses with nontrivial examples.<br/

Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals

Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of −12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59∼74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect