Active damping methods to suppress the payload swing by a 3-degree of freedom cable-driven parallel robot

Marine cranes play an important role in many hoisting scenes. However, due to the ship motion excitation and crane operation, the payload swing is unavoidable, hence putting the operators in danger. Thus, this paper proposes two active damping methods to suppress the payload swing by a 3-DOF cable-driven parallel robot (CDPR). First, the structure of the 3-DOF CDPR is briefly introduced. Then, the dynamic model of the system is modeled as a constrained spherical pendulum with moving base excitations. Meanwhile, inspired by the air damping phenomenon, two active damping methods are presented to damp the payload swing naturally and smoothly. Furthermore, the payload swing under ship motion excitation and external hitting disturbances is analyzed, the influence mechanism of system damping parameters on the payload anti-swing is discussed, and the robustness of the proposed damping methods is verified. By theoretical analysis and numerical simulations, some interesting discoveries are revealed: (1) the proposed damping methods can effectively suppress the big offset of payload swing angle inducing by external hitting disturbance, but the system might reach a dynamic equilibrium with a tilting hoist cable; (2) the phenomenon of “off-center spherical pendulation” might be induced by improper setting of the damping parameters.     

冗余驱动仿下颌运动机器人的机构设计及轨迹规划

为实现人体下颌的运动,针对下颌系统的冗余特性和颞下颌关节运动的独特性,提出一种新型的冗余驱动的仿下颌运动机器人.首先,根据人体下颌运动机理以及仿生设计参数对仿下颌运动机器人进行样机设计.然后,基于虚拟仿真软件,对冗余驱动的仿下颌运动机器人进行轨迹规划.最后,在样机上进行下颌功能运动实验,分别模拟下颌的开闭、前后和侧方运动.结果证明该仿下颌运动机器人能够实现人体下颌的运动,特别是颞下颌关节的运动.     

Designing and testing a calibrating procedure for combining the coordination systems of a handling robot and a stationed video camera

The paper discusses some of the results of designing and testing a calibrating procedure with important implications in contemporary robotics. This procedure is also mentioned in the literature as the “procedure for calibrating by matching the coordination systems of a robot and a stationed video camera”. The procedure is tested by a training robotechnic system, which consists of an anthropomorphic robot with five stages of freedom and a stationed video camera that can identify solid stationary objects. The results of this experimental testing are used to create and adopt some new methods for laboratory exams in robotics for students of FA in a Technical University and for developing further research on calibrating procedures in robotics needed for the industry.     

Design and performance of an intelligent predictive controller for a six-degree-of-freedom robot using the Elman network

The aim of this paper was to propose a recurrent neural network-based predictive controller for robotic manipulators. A neural network controller for a six-joint Stanford robotic manipulator was designed using the generalized predictive control (GPC) and the Elman network. The GPC algorithm, which is a class of digital control method, requires long computational time. This is a disadvantage in real-time robot control; therefore, the Elman network controller was designed to reduce processing time by avoiding the highly mathematical and computational complexity of the GPC. The main reason for choosing the Elman network, amongst several neural network algorithms, was that the presence of feedback loops have a profound impact on the learning capability of the network. The designed neural network controller was able to recover quickly because of its significant generalization capability, which allowed it to adapt very rapidly to changes in inputs. The performance of the controller was also shown graphically using simulation software, including the dynamics and kinematics of the robot model.