Space research is of great significance to increasingly decentralized and distributed space systems, and path planning in space systems has become a research hotspot for maintaining its safety, security, and reliability. To explore the passable path connecting the starting point and the target point, and optimize a smooth trajectory that can be tracked by UAVs (Unmanned Aerial Vehicle) in 3D space, a Skeleton Extraction and Greedy Algorithm based Path Planning has been proposed to guide the flight of UAVs. First, the RRT (Rapidly Exploring Random Tree) has been introduced for path search. To speed up the path search process, the spatial skeleton extraction method has been introduced to calculate the skeleton of free space. The greedy algorithm has been utilized to increase the RRT expansion and reduce unnecessary bends in the path. The Skeleton Extraction and Greedy Algorithm based Lazy RRT and RRT-Connect have been proposed to build compared experiments. Second, the minimum snap has been applied to generate a smooth flight trajectory, and the flight time is allocated according to the distance between the waypoints. Third, the UAV Simulink model has been established, and the spatial position of the optimized trajectory is tracked. The experimental results prove that the skeleton extraction can significantly speed up the search process, and greedy algorithm can shorten the path length effectively. The minimum snap combined with the time allocation strategy can produce a smooth and feasible path. The UAV Simulink model also proves that the classic PD (Proportion-Differentiation) controller can accurately track the generated trajectory. Greedy algorithm and skeleton extraction reduce the average path length of RRT by 9.780% and 8.251%, respectively. Greedy algorithm and skeleton extraction reduce the average path length of RRT, Lazy RRT and RRT-Connect by about 10%-13%. Greedy algorithm and skeleton extraction reduce the time consumption of RRT by 62.781% and 36.276%, respectively.

To accurately detect dynamic objects in dynamic scenes (DSs), a detection framework equipped with visual-based measurement methods has been proposed in this article. First, to segment dynamic objects in real time, the real-time instance segmentation network, You Only Look At CoefficienTs (YOLACT), has been introduced. Second, the geometric constraints have been utilized to further filter the missing dynamic feature points outside the segmentation mask. The dense optical flow method with adaptive threshold has been introduced to detect the missing dynamic objects driven by humans. Third, a background inpainting strategy has been proposed to restore the features occluded by dynamic objects. In order to verify the effectiveness of the dynamic object detection, the proposed method has been embedded in the visual simultaneous localization and mapping (SLAM) system to improve its performance in dynamic environments. Experiments performed on the Technische Universität München (TUM) and KITTI datasets have proved that the proposed detection method has an excellent performance in DSs, which is of great significance to improve the robustness of the SLAM system.

Electroencephalogram (EEG), obtained by wearable devices, can realize effective human health monitoring. Traditional methods based on artificially-designed features have achieved valid results in EEG-based recognition, and numerous studies start to apply deep learning techniques in this area. In this paper, we propose a coincidence filtering-based method to build a connection between artificial features-based methods and convolutional neural networks (CNNs), and design CNNs through simulating the information extraction pattern of artificial features-based methods. Based on this method, we propose a novel, simple, and effective CNNs structure for EEG-based classification. We implement two experiments to obtain EEG data, and perform experiments based on the two health monitoring tasks. The results illustrate that the proposed network can achieve prominent average accuracy on the emotion recognition and fatigue driving detection task. Due to its generality, the proposed framework design of CNNs is expected to be useful for broader applications in health monitoring areas.

This article considers the decentralized stabilization problem of continuous-time nonlinear systems subject to unmatched interconnections and asymmetric input constraints. Initially, with the nonquadratic value functions being introduced to constrained auxiliary subsystems, the decentralized stabilization problem is converted into an array of nonlinear optimal control problems. It is proved that all solutions of these nonlinear optimal control problems together assure asymptotic stability of the entire system. Then, in the framework of adaptive critics, the critic-only architecture is built to solve the Hamilton–Jacobi–Bellman equations associated with these solutions. The critic-only architecture is implemented via critic neural networks (NNs) with their weight vectors being tuned through an improved gradient descent method. A remarkable feature of the present gradient descent approach is that it simultaneously utilizes previously stored and instantaneous state data, which makes the persistence of excitation conditions relaxed. After that, with Lyapunov’s techniques being employed, asymptotic stability of the closed-loop auxiliary subsystems and uniform ultimate boundedness of the critic NNs’ weight estimation errors are demonstrated. Finally, simulations of an unmatched interconnected nonlinear plant are provided to validate the present decentralized control method.

We study the decentralized event-driven control problem of nonlinear dynamical systems with mismatched interconnections and asymmetric input constraints. To begin with, by introducing a discounted cost function for each auxiliary subsystem, we transform the decentralized event-driven constrained control problem into a group of nonlinear $H_{2}$ -constrained optimal control problems. Then, we develop the event-driven Hamilton–Jacobi–Bellman equations (ED-HJBEs), which arise in the nonlinear $H_{2}$ -constrained optimal control problems. Meanwhile, we demonstrate that all the solutions of the ED-HJBEs together keep the overall system stable in the sense of uniform ultimate boundedness (UUB). To solve the ED-HJBEs, we build a critic-only architecture under the framework of adaptive critic designs. The architecture only employs critic neural networks and updates their weight vectors via the gradient descent method. After that, based on the Lyapunov approach, we prove that the UUB stability of all signals in the closed-loop auxiliary subsystems is assured. Finally, simulations of an illustrated nonlinear interconnected plant are provided to validate the present designs.