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In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the optimization variables within the cost function. This cost function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic feasibility, similarity to the reference trajectory, and trajectory smoothness. To enhance prediction accuracy, we combine physics-based and pattern-based methods for predicting obstacle movements. These predicted movements are then integrated into the online trajectory replanning framework, significantly enhancing the system's safety. Our approach provides a robust solution for navigating dynamic environments, ensuring both optimal and secure UAV operation. [ABSTRACT FROM AUTHOR]

Drawing from the characteristics of centipedes, such as their low center of gravity, high stability in movement, adaptability to complex terrains, and ability to continue moving even after losing a limb, this paper designs a self-reconfigurable centipede-type rescue robot with relatively high stability while moving. The robot's body can lift and traverse higher obstacles, and its multi-segmented structure enables self-disconnection and reconstruction for docking. Moreover, the proposed robot is adept at navigating diverse terrains and surmounting obstacles, equipped with a camera sensor facilitating life recognition, terrain surveying, scene understanding, and obstacle avoidance. Its capabilities prove advantageous for achieving challenging ground rescue missions. Motion stability tests, conducted across various terrains, showcase the robot's ability to maintain a consistent movement path in rugged environments. Operating with a leg lift height of 0.02 m, the robot achieves a speed of 0.09 m per second. In simulated damaged conditions, the robot demonstrates the capacity to disconnect and reconnect its limbs swiftly, restoring movement capabilities within a single second. During environmental perception tasks, the robot processes and analyzes environmental data in real time at a rate of approximately 15 frames per second, with an 80% confidence level. With an F1 score exceeding 93% and an average precision rate surpassing 98%, the robot showcases its reliability and efficiency. [ABSTRACT FROM AUTHOR]

We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.

PM2.5 can bring serious harm to people's health and life because it easily causes cardiovascular disease and increases the risk of cancer. Hence, monitoring PM2.5 real‐timely becomes a key problem in environmental protection. Towards this end, this paper proposes an improved picture‐based prediction method of PM2.5 concentration using artificial neural network (ANN). Firstly, the weather image is transformed into Hue, Saturation, Value (HSV) color space to extract its saturation map, then the corresponding spatial and transform‐based entropy features of image space are extracted. Secondly, the PM2.5 concentration model is built based on the two extracted features from the weather image using Artificial Neural Network (ANN) theory. Thirdly, an ANN model is trained using the pre‐processed data. The training parameters and conditions are also explored through multiple experiments to achieve the best model accuracy. Experimental results show that the model has the best prediction effect when comparing to other state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]