Integrating hybrid active-passive communications into cognitive radio can achieve a spectrum- and energy-efficiency information transmission, while the resource allocation has not been well studied particularly for the network with multiple secondary users (also termed as the Internet of Things (IoT) users). In this article, we formulate an optimization problem to maximize the energy efficiency of all the IoT nodes in a cognitive wireless-powered hybrid active-passive communication network by taking the interference from the IoT node to the primary link, the energy causality constraint, and the minimum throughput constraint per IoT node. By using the Dinkelbach method and introducing auxiliary variables, we devise an iterative algorithm to optimally solve the formulated problem. Computer simulations are provided to validate the quick convergence of the iterative algorithm and the advantages of the proposed scheme in terms of the energy efficiency.
Aiming at the visual measurement requirements of space manipulators for grasping non-cooperative targets, a space target docking ring recognition and center point positioning method based on Tiny Darknet YOLOv3 fusion CenterNet is proposed. First, training network model based on open source ImageNet VOC 2007 and self-built spatial non-cooperative target data set and use optimized Tiny Darknet YOLOv3 fusion CenterNet deep learning algorithm to identify space target docking and obtain two-dimensional pixel coordinates of the docking center point; secondly, using the EnsensoN10-408-18 depth camera to obtain the 3*3 neighborhood data of the depth value corresponding to the center point and calculate the neighborhood weighted optimal value to get docking center spatial coordinates in the camera coordinate system. Combined with the hand-eye calibration relationship, the docking center spatial coordinates are converted to the UR5 manipulator base coordinate system. A ground verification system for manipulator to capture the target was built to test the target docking ring identification and center point positioning, and the accuracy error evaluation is completed based on the OptiTrack motion capture global measurement benchmark system. The experimental results show that target positioning accuracy is better than 10mm, and real-time data update rate is better than 2Hz in the dynamic approximation process from 1.5m to 0.2m, which can effectively solve the slow speed and poor accuracy caused by the influence of environmental lighting, target surface material, target attitude scale changes and other factors in traditional feature extraction methods. It lays a foundation for the safe arrival, capture and other manipulator fine operations.
In this paper, in order to perform the detumbling operation after capturing a non-cooperative satellite, a control framework for dual-arm space robot is presented. First, using a parametrized description with normalized time and control torque limitations, a time-optimal detumbling strategy for the target is given. The dynamics formulation of the complete system in post-capture phase, including multi-arm space robot and the target is then illustrated, laying the groundwork for coordinated controller design. A coordinated controller is proposed to conduct the pre-planned detumbling strategy, while internal force control and load distribution are two supplementary objectives that can be achieved with the given controller. The feasibility and effectiveness of the proposed method are demonstrated by simulation results for detumbling a target with rotating motion utilizing two 7 degree-of-freedom kinematically redundant space manipulators.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.