The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the disadvantages of two single sensors and, in fusing the advantages of both sensors, significantly improve the quality of the images. The fusion of infrared and visible images is widely used in agriculture, industry, medicine, and other fields. In this study, firstly, the architecture of mainstream infrared and visible image fusion technology and application was reviewed; secondly, the application status in robot vision, medical imaging, agricultural remote sensing, and industrial defect detection fields was discussed; thirdly, the evaluation indicators of the main image fusion methods were combined into the subjective evaluation and the objective evaluation, the properties of current mainstream technologies were then specifically analyzed and compared, and the outlook for image fusion was assessed; finally, infrared and visible image fusion was summarized. The results show that the definition and efficiency of the fused infrared and visible image had been improved significantly. However, there were still some problems, such as the poor accuracy of the fused image, and irretrievably lost pixels. There is a need to improve the adaptive design of the traditional algorithm parameters, to combine the innovation of the fusion algorithm and the optimization of the neural network, so as to further improve the image fusion accuracy, reduce noise interference, and improve the real-time performance of the algorithm.

This paper presents a novel knowledge-based genetic algorithm to generate a collision-free path in complex environments. The proposed algorithm infuses specific domain knowledge into robot path planning through the development of five problem-specific operators that integrate a local search technique to improve efficiency. In addition, the proposed algorithm introduces a unique and straightforward representation of the robot path and an effective method for evaluating the path quality and accurately detecting collisions. The proposed algorithm is capable of finding optimal or sub-optimal robot paths in both static and dynamic environments. Simulation and experimental studies are conducted to showcase the effectiveness and efficiency of the proposed algorithm. Furthermore, a comparative study is performed to highlight the indispensable role of specialized genetic operators within the proposed algorithm in solving the path planning problem.

Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this article, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cannot provide a satisfactory solution to real-time responses to sudden environmental changes. The neurodynamic model is incorporated into the feature learning method that can use environmental information to improve path planning strategies. Task assignment and collision-free rescue trajectory are generated through robot poses and the dynamic landscape of neural activity. A dual-channel scale filter, a neural activity channel, and a secondary distance fusion are employed to extract and filter feature neurons. After completion of the feature learning process, a neurodynamics-based feature matrix is established to quickly generate the new heuristic rescue paths with parameter-driven topological adaptability. The proposed FLBBINN aims to reduce the computational complexity of the neural network-based approach and enable the feature learning method to achieve real-time responses to environmental changes. Several simulations and experiments have been conducted to evaluate the performance of the proposed FLBBINN. The results show that the proposed FLBBINN would significantly improve the speed, efficiency, and optimality for rescue operations.

Fish schools present high-efficiency group behaviors through simple individual interactions to collective migration and dynamic escape from the predator. The school behavior of fish is usually a good inspiration to design control architecture for swarm robots. In this article, a novel fish-inspired self-adaptive approach is proposed for collective escape for the swarm robots. In addition, a bio-inspired neural network is introduced to generate collision-free escape robot trajectories through the combination of attractive and repulsive forces. Furthermore, to cope with dynamic environments, a neurodynamics-based self-adaptive mechanism is proposed to improve the self-adaptive performance of the swarm robots in the changing environment. Similar to fish escape maneuvers, simulation and experimental results show that the swarm robots are capable of collectively leaving away from the threats. Several comparison studies demonstrated that the proposed approach can significantly improve the effectiveness and efficiency of system performance, and the flexibility and robustness in complex environments.

As fundamental prerequisites for the operation and maintenance (O&M) of hydroelectric units, situation awareness and health management have emerged as research hotspots in recent years. Bioinspired intelligence, with advantages such as high efficiency, environmental adaptability, robust performance, and transferability, provides new research ideas, methods, and applications for the O&M of hydroelectric units, especially in situation awareness and health management. This paper reviews the prospects, current applications, and technical challenges of bioinspired intelligence in situation awareness and health management of hydroelectric units from the perspective of reliability-centered maintenance (RCM). First, the technical requirements and features of situation awareness and health management for hydroelectric units in RCM are elucidated. Next, the technical frameworks of hydroelectric units are reviewed from the perspective of bioinspired intelligence. A detailed discussion is then provided regarding the relevant implementation strategies in multiple domains, including real-time monitoring, multi-source signal fusion, state characteristic extraction, intelligent health diagnostics, maintenance decision-making optimization, and smart O&M systems. Finally, future trends and development opportunities in applying bioinspired intelligence to situation awareness and health management of hydroelectric units are proposed: integrating the advantages of bioinspired intelligence with the engineering requirements of RCM and innovating approaches for intelligent O&M, which would provide further support for safe, reliable, and efficient energy systems.