Technical advancement has propelled the proliferation of unmanned vehicles. Out of the multiple paths between origin (O) and destination (D), the optimal O-D path should be selected in the light of travel distance, travel time, fuel cost and pollutant emissions. This paper proposes a dynamic path planning strategy based on fuzzy logic (FL) and improved ant colony optimization (ACO). Firstly, the classic ACO was improved into the rank-based ant system. The rank-based ant system works well in static environments, but cannot adapt well to dynamic environments. Considering the difficulty in accurate digitization of dynamic factors, the improved ACO was integrated with the FL into the fuzzy logic ant colony optimization (FLACO) to find the optimal path for unmanned vehicles. Finally, the FLACO, the classic ACO and the improved ACO were separately applied to find the optimal path in a road network, with a novel concept called virtual path length. The results show that the FLACO output the shortest virtual path among the three algorithms, i.e. identified the most cost-effective path. This mean the FLACO can find the most efficient and safe path for unmanned vehicles in a dynamic manner.

Robotics research has a unique allure for both academia and the industry due to its potential for groundbreaking innovation and real-world applications

The principle of the image fusion is to integrate complementary information of the heterogeneous images to obtain a fused image that is more in line with the visual effect of the human eyes. However, most decomposition methods cannot distinguish the textures and edges in an image, which is easy to produce the halo artifacts around edges. In this paper, we proposed a novel image decomposition strategy (co-occurrence analysis shearlet transform, CAST) to preprocess the input images depending on the co-occurrence statistic information to generate the base layer and detail layer components. In order to improve the sparseness of the base layer, the classified sparse dictionary in the measurement domain is introduced to enhance the robustness of incorrect registration. As for the detail layers, the adaptive dual-channel PCNN model is adopted as the fusion rule, in which the neurons are activated by the improved spatial frequency operator (ISF), and the model uses the sum of the improved weighted Laplacian energies (IWSWL) as the adaptive linking strength. Finally, the fused image can be generated by the inverse CAST. Based on the combination of the sparseness of the classified dictionary and the visual characteristics of PCNN model, the more valuable information of the source images can be fused, so that the final fused images conform to the human visual system. Qualitative and quantitative experimental results demonstrate the superiority of the proposed method over other typical fusion techniques on the publicly available datasets.

In response to the issue of virus contamination in the cold-chain warehouse or hospital environment under the influence of the COVID-19, we propose the design work of a disinfection robot based on the UVC radiation mechanism using the low-computational path optimization at-the-edge. To build a surface disinfection robot with less computing power to generate a collision-free path with shorter total distance in studies, a 2D map is used as a graph-based approach to automatically generate a closed-loop disinfection path to cover all the accessible surfaces. The discrete disinfection points from the map are extracted with effective disinfection distances and sorted by a nearest-neighbor (NN) search over historical trajectory data and improved A * algorithm to obtain an efficient coverage path to all accessible boundaries of the entire area. The purpose of improved A * algorithm with NN is not to find the optimal path solution but to optimize one with reasonable computing power. The proposed algorithm enhances the path-finding efficiency by a dynamically weighted heuristic function and reduces the path turning angles, which improves the path smoothness significantly requiring less computing power. The Gazebo simulation is conducted, and the prototype disinfection robot has been built and tested in a real lab environment. Compared with the classic A * algorithm, the improved A * algorithm with NN has improved the path-finding efficiency and reduced the path length while covering the same area. Both the simulation and experimental results show that this approach can provide the design to balance the tradeoffs among the path-finding efficiency, smoothness, disinfection coverage, and computation resources.

Many urban rail transit (URT) systems adopt the DC traction power supply system. Because of the impedance and incomplete ground insulation of the running track, it is inevitable for a part of the traction current to flow into the ground from the track, creating the stray current. This type of current causes great safety hazards to the metal structures in and near the URT system. Considering the power supply mode of the URT, this paper explores the different resistances in each power supply section under unilateral power supply and bilateral power supply. Then, the defects of the current discharge method were identified in the context of stray current protection. To solve these defects, the backpropagation neural network (BPNN) was adopted to build a discharge flow prediction model. On this basis, an intelligent current monitoring system was established for the URT. Finally, the authors simulated the impact of each factor on stray current, and verified the reliability and stability of the proposed monitoring system. Compared with predicted values and the actual values, the prediction agrees with the actual data very well.

For space target images captured by a sky-based visible light camera, various conditions are influenced by the imaging system itself and the observation environment; these conditions include uneven image background intensity, complex noise, stray light composition, and diverse target forms. A mean wavelet transform algorithm is proposed. This algorithm initially performs mean filtering and wavelet transform filtering on the noise-containing space target images and then performs a wavelet inverse transform on the filtered results to generate the final image. The experimental results show that our algorithm has good denoising performance and can effectively maintain the image details.