This study presents the SSALC (enhanced sparrow search algorithm), a novel approach for improving the efficiency of industrial robots. It incorporates logistic chaotic sequences and crisscross strategy optimization. The logistic chaotic sequence generates a more uniformly distributed population, while the nonlinear inertia weight controls the algorithm’s step size. The crisscross strategy enhances the position-updating mechanism, and the horizontal crossover operation improves global optimization capability. Furthermore, the vertical crossover operation allows for dimension mutation in some individuals, reducing susceptibility to local optima. Extensive testing against 11 CEC benchmark functions, complemented by the Wilcoxon rank sum test, confirms the effectiveness of the approach. Results demonstrate the SSALC’s fast convergence, high accuracy, and improved global search ability in addressing both unimodal and multimodal optimization challenges.

The solar array wing deployment of orbiting satellites cannot be performed due to power failure of the connector caused by uncertain loads such as high temperature or vibration in the launching process of the spacecraft. There is currently a lack of suitable unlocking solutions for solar wing re-unfolding. This paper proposes a solution in which an extensible continuum robot (ECR) carrying the unlocking device enters the gap between the satellite and the solar wing, re-unlocking the solar wing. This solution effectively leverages the advantages of ECR collision buffering and adaptable maneuverability within confined space. In response to the proposed solution, the designed ECR with two segments helical spring structure features scalability, hollowness, lightweight, and a big length-diameter ratio. To perform the critical unlocking task, an end effector with the function of loosening and unplugging the aerospace connector for communication is designed based on the drive device away from itself to reduce the inertia of the manipulator. The information from the cameras and force sensors is used to estimate the extent of task execution. We establish an experimental setup to simulate the process of unlocking. The results validate that the ECR successfully accesses the gap (65mm) and accomplishes the unlocking task. The ECR has great application potential for on-orbit service.

Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow. With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs. However, the performance of most existing surveillance systems is susceptible to challenging complex traffic scenes (e.g., object occlusion, pose variation, and cluttered background). Moreover, existing related research is mainly on a single video sensor node, which is incapable of addressing the surveillance of traffic road networks. Accordingly, we present a review of the literature on the video-based vehicle surveillance systems in ITSs. We analyze the existing challenges in video-based surveillance systems for the vehicle and present a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques. Then, different methods are reviewed and discussed with respect to each module. Applications and future developments are discussed to provide future needs of ITS services.

This paper proposes a novel approach to visual tracking of moving objects based on the dynamic coupled conditional random field (DcCRF) model. The principal idea is to integrate a variety of relevant knowledge about object tracking into a unified dynamic probabilistic framework, which is called the DcCRF model in this paper. Under this framework, the proposed approach integrates spatiotemporal contextual information of motion and appearance, as well as the compatibility between the foreground label and object label. An approximate inference algorithm, i.e., loopy belief propagation, is adopted to conduct the inference. Meanwhile, the background model is adaptively updated to deal with gradual background changes. Experimental results show that the proposed approach can accurately track moving objects (with or without occlusions) in monocular video sequences and outperforms some state-of-the-art methods in tracking and segmentation accuracy.

Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow. With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs. However, the performance of most existing surveillance systems is susceptible to challenging complex traffic scenes (e.g., object occlusion, pose variation, and cluttered background). Moreover, existing related research is mainly on a single video sensor node, which is incapable of addressing the surveillance of traffic road networks. Accordingly, we present a review of the literature on the video-based vehicle surveillance systems in ITSs. We analyze the existing challenges in video-based surveillance systems for the vehicle and present a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques. Then, different methods are reviewed and discussed with respect to each module. Applications and future developments are discussed to provide future needs of ITS services.