A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments

This article is concerned with the path planning of mobile robots in dynamic environments. A new path planning strategy is proposed by integrating the improved ant colony optimization (ACO) and dynamic window approach (DWA) algorithms. An improved ACO is developed to produce a globally optimal path for mobile robots in static environments. Through improvements in the initialization of pheromones, heuristic function, and updating of pheromones, the improved ACO can lead to a shorter path with fewer turning points in fewer iterations. Based on the globally optimal path, a modified DWA is presented for the path planning of mobile robots in dynamic environments. By deleting the redundant nodes, optimizing the initial orientation, and improving the evaluation function, the modified DWA can result in a more efficient path for mobile robots to avoid moving obstacles. Some simulations are conducted in different environments, which confirm the effectiveness and superiority of the proposed path planning algorithms

On Fixed-Point Smoothing for Descriptor Systems with Multiplicative Noise and Single Delayed Observations

Optimal fixed-point smoothing problem for the descriptor systems with multiplicative noises is considered, where instantaneous and delayed observations are available. Standard singular value decomposition is used to give the restricted equivalent delayed system, where the observations also include two different types of measurements. Reorganized innovation lemma and projection theorem are used to give the fixed-point smoother for the restricted equivalent delayed system. The fixed-point smoother is given in terms of recursive Riccati equations

A new approach to fault-line selection of small current neutral grounding system

In this paper, a new approach combining BP neural network with fuzzy Petri net (FPN) is developed to deal with the issue of fault-line selection of the small current neutral grounding system. First, the preliminaries of the FPN are briefly introduced. Then, the model of the fault-line selection is detailedly described to explain the new feature representation that fuses multiple fault features of the lines, including the wavelet energy, the active component and the fifth harmonic component. Finally, the simulation model of the fault-line selection is constructed, and the simulation experiments are carried out to verify the effectiveness of the new approach, which could be superior to the traditional fault-line selection approaches

A cooperative stochastic configuration network based on differential evolutionary sparrow search algorithm for prediction

Stochastic configuration network (SCN) is a powerful prediction model whose performance is significantly influenced by the configuration of the network parameters. To improve the prediction accuracy of the network, a cooperative stochastic configuration network (CSCN) based on a novel differential evolutionary sparrow search algorithm (DESSA), termed as DESSA-CSCN, is proposed. In the CSCN, the number of hidden layer nodes is adaptively adjusted according to the number of iterations, and the parameters of hidden nodes are cooperatively optimized by using a population-based metaheuristic algorithm. A sparse matrix is introduced to mitigate parameter overfitting caused by the increased number of hidden layer nodes. During parameter optimization, the fitness function is constructed by using the supervision mechanism of the SCN, and the DESSA is utilized as the metaheuristic algorithm to update the weights and biases. In order to verify the effectiveness of the DESSA-CSCN, several simulation experiments have been conducted. The performance of the DESSA is evaluated by the CEC2017 test suit, and the simulation results show that the DESSA exhibits better convergence accuracy and can jump out of local optima more effectively than other algorithms. The performance of the DESSA-CSCN is evaluated by 4 datasets from KEEL, and the simulation results indicate that the DESSA-CSCN achieves better prediction accuracy and faster prediction speed than other models

Design and implementation of an automatic charging system for intelligent patrol robot

This paper is concerned with the design and implementation of an automatic charging system for the intelligent patrol robot. An economic and practical method combined with the infrared sensor and laser sensor is developed to realize the accurate automatic charge docking. The phase-shifted full-bridge ZVS-PWM converter is adopted to design an automatic charging pile, which uses a constant voltage limited current charging mode and improve the efficiency of the charging. Several simulations and experiments are implemented to test the automatic charge docking system, and the results could demonstrate the effectiveness and superiority of the system

Peptide-anchored neutrophil membrane-coated biomimetic nanodrug for targeted treatment of rheumatoid arthritis

Abstract Macrophage polarization determines the production of cytokines that fuel the initiation and evolution of rheumatoid arthritis (RA). Thus, modulation of macrophage polarization might represent a potential therapeutic strategy for RA. However, coordinated modulation of macrophages in the synovium and synovial fluid has not been achieved thus far. Herein, we develop a biomimetic ApoA-I mimetic peptide-modified neutrophil membrane-wrapped F127 polymer (R4F-NM@F127) for targeted drug delivery during RA treatment. Due to the high expression of adhesion molecules and chemokine receptors on neutrophils, the neutrophil membrane coating can endow the nanocarrier with synovitis-targeting ability, with subsequent recruitment to the synovial fluid under the chemotactic effects of IL-8. Moreover, R4F peptide modification further endows the nanocarrier with the ability to target the SR-B1 receptor, which is highly expressed on macrophages in the synovium and synovial fluid. Long-term in vivo imaging shows that R4F-NM@F127 preferentially accumulates in inflamed joints and is engulfed by macrophages. After loading of the anti-inflammatory drug celastrol (Cel), R4F-NM@F127-Cel shows a significant reduction in hepatotoxicity, and effectively inhibits synovial inflammation and alleviates joint damage by reprogramming macrophage polarization. Thus, our results highlight the potential of the coordinated targeted modulation of macrophages as a promising therapeutic option for the treatment of RA

Pt-Sensitized In₂O₃ Nanotubes for Sensitive Acetone Monitoring

Detecting 1 ppm acetone at high humidity is essential for a noninvasive diabetes diagnosis. Metal oxide gas sensors are a promising technology to achieve high sensitivity acetone monitoring. Here, we fabricated Pt-sensitized In2O3 nanotubes, and the gas-sensing performance was tested against eight gases. The fiber structure contributes to the uniform dispersion of Pt onto the In2O3. Pt-sensitized In2O3 nanotubes have lower optimal operating temperatures and higher sensitivity and selectivity than those of the In2O3 nanotubes. The 0.75 wt % Pt-In2O3 sensor has the maximum sensitivity (113) to 10 ppm acetone at 300 °C; the response and response time to 1 ppm acetone are 19.9 and 10 s, respectively. The response to 1 ppm acetone still has 9.83 at the relative humidity of 83%. It also has a low limit of detection (8.4 ppb) and good long-term stability (30 days). These results illustrate that Pt-sensitized In2O3 nanotubes have the potential for a noninvasive diabetes diagnosis