The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize autonomous positioning and navigation, path-planning technology should plan collision-free and smooth paths for mobile robots in obstructed environments, which requires path-planning algorithms with a certain degree of intelligence. Metaheuristic algorithms are widely used in various optimization problems due to their algorithmic intelligence, and they have become the most effective algorithm to solve complex optimization problems in the field of mobile robot path planning. Based on a comprehensive analysis of existing path-planning algorithms, this paper proposes a new algorithm classification. Based on this classification, we focus on the firefly algorithm (FA) and the cuckoo search algorithm (CS), complemented by the dragonfly algorithm (DA), the whale optimization algorithm (WOA), and the sparrow search algorithm (SSA). During the analysis of the above algorithms, this paper summarizes the current research results of mobile robot path planning and proposes the future development trend of mobile robot path planning.

Forest fires have devastating impacts on ecology, the economy, and human life. Therefore, the timely detection and extinguishing of fires are crucial to minimizing the losses caused by these disasters. A novel dual-channel CNN for forest fires is proposed in this paper based on multiple feature enhancement techniques. First, the features’ semantic information and richness are enhanced by repeatedly fusing deep and shallow features extracted from the basic network model and integrating the results of multiple types of pooling layers. Second, an attention mechanism, the convolutional block attention module, is used to focus on the key details of the fused features, making the network more efficient. Finally, two improved single-channel networks are merged to obtain a better-performing dual-channel network. In addition, transfer learning is used to address overfitting and reduce time costs. The experimental results show that the accuracy of the proposed model for fire recognition is 98.90%, with a better performance. The findings from this study can be applied to the early detection of forest fires, assisting forest ecosystem managers in developing timely and scientifically informed defense strategies to minimize the damage caused by fires.

Carrier phase measurements currently play a crucial role in achieving rapid and highly accurate positioning of global navigation satellite systems (GNSS). Resolving the integer ambiguity correctly is one of the key steps in this process. To address the inefficiency and slow search problem during ambiguity solving, we propose a single-frequency GNSS integer ambiguity solving based on an adaptive genetic particle swarm optimization (AGPSO) algorithm. Initially, we solve for the floating-point solution and its corresponding covariance matrix using the carrier-phase double difference equation. Subsequently, we decorrelate it using the inverse integer Cholesky algorithm. Furthermore, we introduce an improved fitness function to enhance convergence and search performance. Finally, we combine a particle swarm optimization algorithm with adaptive weights to conduct an integer ambiguity search, where each generation selectively undergoes half-random crossover and mutation operations to facilitate escaping local optima. Comparative studies against traditional algorithms and other intelligent algorithms demonstrate that the AGPSO algorithm exhibits faster convergence rates, improved stability in integer ambiguity search results, and in practical experiments the baseline accuracy of the solution is within 0.02 m, which has some application value in the practical situation of short baselines.

In this paper, an improved MobileNetV3-Small algorithm model is proposed for the problem of poor real-time wildfire identification based on convolutional neural networks (CNNs). Firstly, a wildfire dataset is constructed and subsequently expanded through image enhancement techniques. Secondly, an efficient channel attention mechanism (ECA) is utilised instead of the Squeeze-and-Excitation (SE) module within the MobileNetV3-Small model to enhance the model’s identification speed. Lastly, a support vector machine (SVM) is employed to replace the classification layer of the MobileNetV3-Small model, with principal component analysis (PCA) applied before the SVM to reduce the dimensionality of the features, thereby enhancing the SVM’s identification efficiency. The experimental results demonstrate that the improved model achieves an accuracy of 98.75% and an average frame rate of 93. Compared to the initial model, the mean frame rate has been elevated by 7.23. The wildfire identification model designed in this paper improves the speed of identification while maintaining accuracy, thereby advancing the development and application of CNNs in the field of wildfire monitoring.

In recent years, the increasing frequency of climate change and extreme weather events has significantly elevated the risk of levee breaches, potentially triggering large-scale floods that threaten surrounding environments and public safety. Rapid and accurate measurement of river surface velocities is crucial for developing effective emergency response plans. Video image velocimetry has emerged as a powerful new approach due to its non-invasive nature, ease of operation, and low cost. This paper introduces the Dynamic Feature Point Pyramid Lucas–Kanade (DFP-P-LK) optical flow algorithm, which employs a feature point dynamic update fusion strategy. The algorithm ensures accurate feature point extraction and reliable tracking through feature point fusion detection and dynamic update mechanisms, enhancing the robustness of optical flow estimation. Based on the DFP-P-LK, we propose a river surface velocity measurement model for rapid levee breach emergency response. This model converts acquired optical flow motion to actual flow velocities using an optical flow-velocity conversion model, providing critical data support for levee breach emergency response. Experimental results show that the method achieves an average measurement error below 15% within the velocity range of 0.43 m/s to 2.06 m/s, demonstrating high practical value and reliability.