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.     

基于认知结构SOAR的机器人路径规划

针对环境未知、目标位置已知情况下机器人路径规划问题,提出了一种基于认知结构SOAR的探索方法。首先,结合箱子移动问题介绍了SOAR的基本概念及求解机制;其次,在讨论了人的行为模型和SOAR的决策过程基础上,设计了基于SOAR-Agent的行为建模方法;最后,利用本文提出的方法,设计长期知识,使机器人能够模拟人在未知动态环境下向目标搜索的行为。通过仿真实验,对比分析了长期知识的完备程度对机器人顺利完成任务的影响,同时也验证了该方法的有效性。     

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.     

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。     

机器人智能化焊接技术发展综述及其在运载火箭贮箱中的应用

针对当前航天产品焊接尚存在焊接自动化水平低、产品质量依赖操作者水平等问题，迫切需要采用机器人智能化焊接技术实现“以机器人换人”。文章阐述了机器人智能化焊接技术的概念，综述了机器人智能化焊接技术发展现状，分析了机器人焊接动态过程的多信息传感技术、知识提取与建模方法以及智能化控制等关键技术。结合上海航天精密机械研究所近年来在机器人自动化焊接技术的研究经验，重点介绍了机器人智能化焊接技术在运载火箭贮箱中取得的技术突破及典型应用成果。     

空间连续型机器人位姿与构型的能量整形控制

针对空间连续型机器人(SCR)位置和姿态机动、连续型机械臂变形控制以及系统受到外界干扰的问题，基于连接与阻尼配置-无源性控制(IDA PBC)方法设计了控制器。通过能量整形得到空间连续型机器人期望的能量函数，再对系统注入阻尼使其渐近稳定。利用非线性干扰观测器估计系统受到的干扰，并对外界干扰进行补偿。仿真结果表明：设计的控制器可以使空间连续型机器人机动到指定的位置和姿态，同时控制连续型机械臂变形为期望的构型，并且具有主动抗干扰的能力。通过选择合适的能量整形系数和阻尼注入系数，可使系统以期望的动态性能到达指定的平衡点。