Muscle synergy analysis for gesture recognition is a fundamental research area in human-machine interaction, particularly in fields such as rehabilitation. However, previous methods for analyzing muscle synergy are typically not end-to-end and lack interpretability. Specifically, these methods involve extracting specific features for gesture recognition from surface electromyography (sEMG) signals and then conducting muscle synergy analysis based on those features. Addressing these limitations, we devised an end-to-end framework, namely Shapley-value-based muscle synergy (SVMS), for muscle synergy analysis. Our approach involves converting sEMG signals into grayscale sEMG images using a sliding window. Subsequently, we convert adjacent grayscale images into color images for gesture recognition. We then use the gradient-weighted class activation mapping (Grad-CAM) method to identify significant feature areas for sEMG images during gesture recognition. Grad-CAM generates a heatmap representation of the images, highlighting the regions that the model uses to make its prediction. Finally, we conduct a quantitative analysis of muscle synergy in the specific area obtained by Grad-CAM based on the Shapley value. The experimental results demonstrate the effectiveness of our SVMS method for muscle synergy analysis. Moreover, we are able to achieve a recognition accuracy of 94.26% for twelve gestures while reducing the required electrode channel information from ten to six dimensions and the analysis rounds from about 1000 to nine.

Plant diseases pose a significant threat to the economic viability of agriculture and the normal functioning of trees in forests. Accurate detection and identification of plant diseases are crucial for smart agricultural and forestry management. Artificial intelligence has been successfully applied to agriculture in recent years. Many intelligent object recognition algorithms, specifically deep learning approaches, have been proposed to identify diseases in plant images. The goal is to reduce labor and improve detection efficiency. This article reviews the application of object detection methods for detecting common plant diseases, such as tomato, citrus, maize, and pine trees. It introduces various object detection models, ranging from basic to modern and sophisticated networks, and compares the innovative aspects and drawbacks of commonly used neural network models. Furthermore, the article discusses current challenges in plant disease detection and object detection methods and suggests promising directions for future work in learning-based plant disease detection systems.

This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.

Soft-bodied robots have the advantages of high flexibility and multiple degrees of freedom and have promising applications in exploring complex unstructured environments. Kinematic coupling exists for the soft robot in a problematic space environment for motion planning between the soft robot arm segments. In solving the soft robot inverse kinematics, there are only solutions or even no solutions, and soft robot obstacle avoidance control is tough to exist, as other problems. In this paper, we use the segmental constant curvature assumption to derive the positive and negative kinematic relationships and design the tip self-growth algorithm to reduce the difficulty of solving the parameters in the inverse kinematics of the soft robot to avoid kinematic coupling. Finally, by combining the improved particle swarm algorithm to optimize the paths, the convergence speed and reconciliation accuracy of the algorithm are further accelerated. The simulation results prove that the method can successfully move the soft robot in complex space with high computational efficiency and high accuracy, which verifies the effectiveness of the research.

Heterogeneous multi-agent task allocation is a key optimization problem widely used in fields such as drone swarms and multi-robot coordination. This paper proposes a new paradigm that innovatively combines graph neural networks and ant colony optimization algorithms to solve the assignment problem of heterogeneous multi-agents. The paper introduces an innovative Graph-based Heterogeneous Neural Network Ant Colony Optimization (GHNN-ACO) algorithm for heterogeneous multi-agent scenarios. The multi-agent system is composed of unmanned aerial vehicles, unmanned ships, and unmanned vehicles that work together to effectively respond to emergencies. This method uses graph neural networks to learn the relationship between tasks and agents, forming a graph representation, which is then integrated into ant colony optimization algorithms to guide the search process of ants. Firstly, the algorithm in this paper constructs heterogeneous graph data containing different types of agents and their relationships and uses the algorithm to classify and predict linkages for agent nodes. Secondly, the GHNN-ACO algorithm performs effectively in heterogeneous multi-agent scenarios, providing an effective solution for node classification and link prediction tasks in intelligent agent systems. Thirdly, the algorithm achieves an accuracy rate of 95.31% in assigning multiple tasks to multiple agents. It holds potential application prospects in emergency response and provides a new idea for multi-agent system cooperation.

This paper investigates the event-triggered consensus for a group of unmanned aerial vehicles (UAVs) with communication faults under the assumption that the position sensors of some individuals are damaged. The objective is to make the UAV group reach consensus in urgent tasks such as obstacle avoidance or evasion. Using the Lyapunov stability theory, sufficient conditions to achieve system consensus are given based on different velocity and position interaction topologies. Considering the limited capabilities of sensors and processors, an event-triggered consensus protocol is adopted to reduce the sampling frequency. Finally, simulation results illustrate the effectiveness of our approach.

Driver drowsiness detection is a critical task for early warning of safe driving, while existing spatial feature-based methods face the challenges of large variations of head pose. This paper proposes a novel approach to integrate the memory mechanism in a multi-granularity deep framework to detect driver drowsiness, and the temporal dependencies over sequential frames are well integrated with the spatial deep learning framework on the frontal faces. The proposed approach includes two steps. First, the spatial Multi-granularity Convolutional Neural Network is designed to utilize a group of parallel Convolutional Neural Network extractors on well-aligned facial patches of different granularities and extract facial representations effectively for large variations of head pose. Furthermore, it can flexibly fuse detailed appearance clues of the main parts and local-to-global spatial constraints. Second, the memory mechanism is set up using a deep long short-term memory network of facial representations to explore long-term relationships with variable length over sequential frames, which is capable of distinguishing the states with temporal dependencies, such as blinking and closing eyes. The proposed approach achieves 90.05% accuracy and about 37 frames per second (FPS) speed on the evaluation set of the National Tsing Hua University Driver Drowsiness Detection dataset, which is applied to the intelligent vehicle for driver drowsiness detection. A dataset named Forward Instant Driver Drowsiness Detection is also built and will be publicly accessible to speed up the study of driver drowsiness detection.

Accurate positional estimation is an essential prerequisite for the regular operation of an autonomous rotary-wing Unmanned Aerial Vehicles (UAV). However, the field of view (FOV) limitation problem of lidar makes it more challenging to locate the rotary-wing UAV in an unknown environment. To address rotor drones with an insufficient FOV and the observation blindness of lidar in complex environments, this paper designs a rotorcraft UAV system based on rotating 3D lidar and proposes a simultaneous localization and mapping algorithm for rotating 3D lidar. The algorithm distinguishes between planar and edge features based on the curvature value of the point cloud first. Then, to reduce the impact caused by the UAV motion and lidar rotation, messages about the Inertial Measurement Unit (IMU) and real-time rotation angles are used to compensate for these motions twice, while the IMU measurements are used for state prediction, and the error-sate iterative extended Kalman filter is used to update the residuals after matching line and surface features with sub-map. Finally, Smoother high-rate odometer data was obtained through IMU pre-integration and a first-order low-pass filter. The experimental results show that the proposed rotating lidar unit in indoor and outdoor conditions makes the rotorcraft UAV have a larger FOV, which not only improves the environmental perception capability and positional estimation accuracy of the rotorcraft but enhances the positioning reliability and flight stability of the rotorcraft UAV in complex environments.

As a key component of the heavy-haul railway system, the rail is prone to damages caused by harsh operating conditions. To secure a safe operation, it is of great essence to detect the damage status of the rail. However, current damage detection methods are mainly manual, so problems such as strong subjectivity, lag in providing results, and difficulty in quantifying the degree of damage are easily generated. Therefore, a new prediction method based on the improved pelican algorithm and channel attention mechanism is proposed to evaluate the stripping of heavy-haul railway rails. By processing the rail vibration acceleration, it predicts the stripping damage degree. Specifically, a comprehensive health index measuring the degree of rail stripping is first established by principal component analysis and correlation analysis to avoid the one-sidedness of a single evaluation index. Then, the convolutional bidirectional gated recursive network is trained and generalized, and the pelican algorithm, improved by multiple hybrid strategies, is used to optimize the hyperparameters in the network so as to find the optimal solution by constantly adjusting the search strategy. The squeeze-excitation channel attention module is then incorporated to re-calibrate the weights of valid features and to improve the accuracy of the model. Finally, the proposed method is tested on a specific rail stripping dataset and a public dataset of PHM2012 bearings, and the generalization and effectiveness performance of the proposed method is proved.

With the rapid development of modern communication and automatic control technologies, unmanned aerial vehicles (UAVs) have increasingly gained importance in both military and civilian domains. Path planning, a critical aspect for achieving autonomous aerial navigation, has consistently been a focal point in UAV research. However, traditional ant colony algorithms need to be improved for the drawbacks of susceptibility to local optima and weak convergence capabilities. Consequently, a novel path planning methodology is proposed based on a dual-strategy ant colony algorithm. In detail, an improved state transition probability rule is introduced, redefining ant movement rules by integrating the state transition strategy of deterministic selection during the iterative process. Additionally, heuristic information on adjacent node distance and mountain height is added to further improve the search efficiency of the algorithm. Then, a new dynamically adjusted pheromone update strategy is proposed. The update strategy is continuously adjusted during the iteration process, which is beneficial to the algorithm’s global search in the early stage and accelerated convergence in the later stage, preventing the algorithm from falling into local optimality and improving its convergence. Based on the above improvements, a new variation of ant colony optimization (ACO) called dual-strategy ACO algorithm is formed. Experimental results prove that dual-strategy ACO has superior global search capabilities and convergence characteristics from four key aspects: path length, fitness values, iteration number, and running time.

Effective management and monitoring of radioactive sources are crucial to ensuring nuclear safety, human health, and the ecological environment. A multi-robot collaborative radioactive source search algorithm based on particle swarm optimization particle filters is proposed. In this algorithm, each robot operates as a mobile observation platform using the latest observations to fuse into particle sampling. At the same time, the particle swarm optimization algorithm moves the particle set to a high-likelihood area to overcome particle degradation. In addition, each particle can learn from the search history of other particles to speed up the convergence of the algorithm. Lastly, the Dynamic Window Approach (DWA) for dynamic window obstacle avoidance is used to avoid obstacles in complex mountainous terrains to achieve efficient source search. Experimental results show that the search success rate of the proposed algorithm is as high as 95%, and its average search time is only 3.43 s.