Current research concerning legged platforms and wheeled platforms primarily focuses on terrain adaptive capability and speed capability,respectively.Compared with wheeled platforms,legged platforms with a closed-chai...Current research concerning legged platforms and wheeled platforms primarily focuses on terrain adaptive capability and speed capability,respectively.Compared with wheeled platforms,legged platforms with a closed-chain mechanism still present deficiencies regarding speed ability.To integrate the advantages of these two types of platforms,a wheel-leg mobile platform with two modes based on a closed-chain mechanism is proposed.First,a closed-chain mechanism that generates a high-knee trajectory in legged mode is designed and analyzed based on kinematic analysis.To improve the platform’s obstacle-surmounting performance,the dimensional parameters of the closedchain mechanism are optimized and the design requirements for the platform’s frame are analyzed.In addition,the particular structure of the leg group is designed to realize transformation between legged mode and wheeled mode.The mobility of the constructed platform is calculated through an obstacle-surmounting probability analysis.The performances of the two motion modes are analyzed and compared by conducting dynamic simulations.Finally,experiments are carried out to verify both the theoretical analyses and the prototype performance.This study proposes a new approach to designing wheel-leg platforms with prominent speed ability and mobility based on a closed-chain mechanism.展开更多
A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper pr...A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.展开更多
This study proposes a novel single-degree-of-freedom(SDOF)hexagonal mechanism that utilizes a cam mechanism to regulate its movement.The hexagonal mechanism adopts a conjugate cam as joint slideways,forming an interna...This study proposes a novel single-degree-of-freedom(SDOF)hexagonal mechanism that utilizes a cam mechanism to regulate its movement.The hexagonal mechanism adopts a conjugate cam as joint slideways,forming an internal closed-chain cam mechanism that is connected to an external 6R linkage via connecting links.By integrating the cam mechanism’s rotation with the driving system,the mechanism can achieve variable length of the central collinear cranks through coupling structure.Moreover,by utilizing the closed-chain cam mechanism and variable parameters of the central cranks,motion control and adjustment of the external structure with three degrees of freedom can be achieved by central driving with SDOF.On the basis of the locomotion planning and kinematics analysis,the structure design and parameter analysis of the hexagonal mechanism are presented.Moreover,the parameters are analyzed using the repeatability of joint trajectories on the basis of locomotion planning that reduces initial motion conditions,ensures motion continuity,and reduces collision energy loss.Finally,the rationality of the design of the hexagonal mechanism with dynamic rolling locomotion is verified through theory,simulation,and experiment.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51735009).
文摘Current research concerning legged platforms and wheeled platforms primarily focuses on terrain adaptive capability and speed capability,respectively.Compared with wheeled platforms,legged platforms with a closed-chain mechanism still present deficiencies regarding speed ability.To integrate the advantages of these two types of platforms,a wheel-leg mobile platform with two modes based on a closed-chain mechanism is proposed.First,a closed-chain mechanism that generates a high-knee trajectory in legged mode is designed and analyzed based on kinematic analysis.To improve the platform’s obstacle-surmounting performance,the dimensional parameters of the closedchain mechanism are optimized and the design requirements for the platform’s frame are analyzed.In addition,the particular structure of the leg group is designed to realize transformation between legged mode and wheeled mode.The mobility of the constructed platform is calculated through an obstacle-surmounting probability analysis.The performances of the two motion modes are analyzed and compared by conducting dynamic simulations.Finally,experiments are carried out to verify both the theoretical analyses and the prototype performance.This study proposes a new approach to designing wheel-leg platforms with prominent speed ability and mobility based on a closed-chain mechanism.
基金Project supported bY the National Natural Science Foundation of China （Grant No.50375085）, and the Natural Science Foundation of Shandong Province （Grant No.Y2002F13）
文摘A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.
基金supported by the National Natural Science Foundation of China(Grant No.52105006).
文摘This study proposes a novel single-degree-of-freedom(SDOF)hexagonal mechanism that utilizes a cam mechanism to regulate its movement.The hexagonal mechanism adopts a conjugate cam as joint slideways,forming an internal closed-chain cam mechanism that is connected to an external 6R linkage via connecting links.By integrating the cam mechanism’s rotation with the driving system,the mechanism can achieve variable length of the central collinear cranks through coupling structure.Moreover,by utilizing the closed-chain cam mechanism and variable parameters of the central cranks,motion control and adjustment of the external structure with three degrees of freedom can be achieved by central driving with SDOF.On the basis of the locomotion planning and kinematics analysis,the structure design and parameter analysis of the hexagonal mechanism are presented.Moreover,the parameters are analyzed using the repeatability of joint trajectories on the basis of locomotion planning that reduces initial motion conditions,ensures motion continuity,and reduces collision energy loss.Finally,the rationality of the design of the hexagonal mechanism with dynamic rolling locomotion is verified through theory,simulation,and experiment.
