空间黏附爬行机器人足端预压力优化与控制

基于仿生干黏附材料的空间足式爬行机器人可附着于航天器外表面并代替宇航员完成舱外巡检、维护等工作,是无人自主化在轨服务与维护的有效途径。为保证足式爬行机器人与航天器的稳定附着,本文针对空间爬行机器人足端在与航天器接触时预压力对黏附稳定性影响的问题,提出一种足端预压力优化与控制方法。首先结合仿生黏附材料特性,建立黏附材料在预压阶段和黏附阶段的稳定性约束;其次在稳定性约束下基于二次规划方法优化足端力,保证预压力足够的同时减小对其他足的脱附力;最后通过基于环境刚度辨识的导纳控制实现优化后的力分配。结果表明：基于环境刚度辨识的导纳控制可实现预压力的柔顺控制,优化后的预压力可减小对其他黏附足的法向脱附力的影响,保证黏附爬行稳定性。     

腿臂融合型在轨装配机器人运动建模与步态规划

针对目前机器人在轨装配存在的作业效率低、空间操作受限、环境感知不完全等问题，提出一种腿臂融合型在轨装配机器人。首先，使用D-H法建立了腿臂融合型装配机器人运动学模型，并对其单个腿部进行正逆运动学分析，得到单个腿各个关节角和足端之间的运动关系；其次，分析了腿臂融合型装配机器人的行走步态和装配步态，行走步态中分析了二步态和三步态，装配步态中分析了单臂装配和双臂装配，使机器人达到平稳的行走和装配；最后，基于ROS(robot operating system)搭建仿真平台并对行走步态进行了仿真验证，结果表明提出的方法能够有效用于该机器人的行走。     

Capture and detumbling control for active debris removal by a dual-arm space robot

Active debris removal (ADR) technology is an effective approach to remediate the proliferation of space debris, which seriously threatens the operational safety of orbital spacecraft. This study aims to design a controller for a dual-arm space robot to capture tumbling debris, including capture control and detumbling control. Typical space debris is considered as a non-cooperative target, which has no specific capture points and unknown dynamic parameters. Compliant clamping control and the adaptive backstepping-based prescribed trajectory tracking control (PTTC) method are proposed in this paper. First, the differential geometry theory is utilized to establish the constraint equations, the dynamic model of the chaser-target system is obtained by applying the Hamilton variational principle, and the compliance clamping controller is further designed to capture the non-cooperative target without contact force feedback. Next, in the post-capture phase, an adaptive backstepping-based PTTC is proposed to detumble the combined spacecraft in the presence of model uncertainties. Finally, numerical simulations are carried out to validate the feasibility of the proposed capture and detumbling control method. Simulation results indicate that the target detumbling achieved by the PTTC method can reduce propellant consumption by up to 24.11%.     

基于深度强化学习的四足机器人后空翻动作生成方法

四足机器人灵巧运动技能的生成一直受到机器人研究者们的广泛关注,其中空中翻滚运动既能展现四足机器人运动的灵活性又具有一定的实用价值.近年来,深度强化学习方法为四足机器人的灵巧运动提供了新的实现思路,利用该方法得到的闭环神经网络控制器具有适应性强、稳定性高等特点.本文在绝影Lite机器人上使用基于模仿专家经验的深度强化学习方法,实现了仿真环境中四足机器人的后空翻动作学习,并进一步证明了设计的后空翻闭环神经网络控制器相比于开环传统位置控制器具有适应性更高的特点.     

电化学加工机器人动态性能设计与优化研究

工业机器人工作空间大、姿态灵活、可配置性高，且成本低，广泛应用于搬运、装配、喷涂和焊接等多个领域。但由于机器人末端轨迹精度不高，低速波动大，在电化学加工领域的应用很少。根据电化学加工低速、高轨迹精度特点，提出采用象限法设定电化学加工机器人工作区域，建立电化学加工机器人动力学优化函数，采用第三代非支配遗传算法NSGA-Ⅲ求解各设计参数最优Pareto解集，通过仿真和实验进行了动态性能测试验证。结果表明在设定工作区域内，电化学加工机器人直线轨迹精度可达0.073 mm，圆弧轨迹精度可达0.145 mm，低速工况下轨迹精度相比传统工业机器人提高近10倍。     

Workspace,stiffness analysis and design optimization of coupled active-passive multilink cable-driven space robots for on-orbit services

The use of space robots (SRs) for on-orbit services (OOSs) has been a hot research topic in recent years. However, the space unstructured environment (i.e.: confined spaces, multiple obstacles, and strong radiation interference) has greatly restricted the application of SRs. The coupled active-passive multilink cable-driven space robot (CAP-MCDSR) has the characteristics of slim body, flexible movement, and electromechanical separation, which is very suitable for extreme space environments. However, the dynamic and stiffness modeling of CAP-MCDSRs is challenging, due to the complex coupling among the active cables, passive cables, joints, and the end-effector. To deal with these problems, this paper proposes a workspace, stiffness analysis and design optimization method for such type of MCDSRs. Firstly, the multi-coupling kinematics relationships among the joint, cables and the end-effector are established. Based on hybrid series-parallel characteristics, the improved coupled active–passive (CAP) dynamic equation is derived. Then, the maximum workspace, the maximum stiffness, and the minimum cable tension are resolved, among them, the overall stiffness is the superposition of the stiffness produced by the active and the passive cable. Furthermore, the workspace, the stiffness, and the cable tension are analyzed by using the nonlinear optimization method (NOPM). Finally, an 8-DOF CAP-MCDSR experiment system is built to verify the proposed modeling and trajectory tracking methods. The proposed modeling and analysis results are very useful for practical space applications, such as designing a new CAP-MCDSR, or utilizing an existing CAP-MCDSR system.     

面向6G移动通信的可重构智能反射表面技术研究综述

伴随移动通信技术的迅猛发展，复杂多变环境和高能耗将是当前以及未来移动通信面临的主要问题，低能耗、高效通信环境自适应调整将是移动通信技术发展的必经之路。可重构智能反射表面（Reconfigurable intelligent surface, RIS）技术的发展，为移动通信提供了低能耗、通信环境自适应可重构服务，满足了复杂通信场景下多样化设备服务需求。本文首先对RIS技术从原理、特点、优势等方面进行了概述；然后针对RIS具体的应用场景，对RIS技术优势进行总结；最后在现有工作基础上，总结了RIS技术可能面临的技术挑战，并进一步论述与展望RIS技术的发展方向。RIS技术将会进一步推动移动通信技术的变革，助力移动通信技术面向未来复杂、智能化场景应用需求。     

多空间机器人协同工作空间的数值求解方法

针对未来在轨服务过程中，可能遇到单个空间机器人难以独立胜任的应用场景，需要多个空间机器人协同操作，但目前针对多空间机器人协同工作空间的研究很少。考虑到解析法计算工作空间难度很大，提出了一种基于蒙特卡洛法的一般协同工作空间数值求解方法。再进一步针对空间机器人协同的特殊性，提出了广义协同工作空间的概念，并给出了数值求解方法。仿真结果表明，基座姿态约束会引起协同工作空间的缩小，同时广义协同工作空间的范围明显大于一般协同工作空间。所得结论可以为空间机器人任务设计与规划提供参考。