Five-bar Robot Research Articles

Study of End-Effector of (2DOF) Five-Bar Robot Positioning: Accuracy, Modeling and Simulation

Study of End-Effector of (2DOF) Five-Bar Robot Positioning: Accuracy, Modeling and Simulation

Data-driven control of a five-bar parallel robot with compliant joints

This paper presents a data-driven control approach for a five-bar robot with compliant joints. The robot consists of a parallel mechanism with compliant elements that introduce uncertainties in modeling and control. To address this fact, it is implemented a model-less data-driven controller based on a Feedforward Neural Network Module (FNNM) that identifies the inverse dynamics of the robot. The FNNM is incorporated into a coordination of Feedforward Control Method (CFCM) to achieve precise trajectory tracking. Experiments compare the compliant joints robot to a bearing-joint robot performing pick-and-place tasks from 0.15 to 3.15 Hz. Results show the compliant robot maintaining trajectory tracking up to 1.25 Hz with a Root Mean Square Error (RMSE) of 9.02 mm.

Design and development of a five-bar robot for research into lower extremity proprioception

SUMMARYAnkle inversion is a common injury of musculoskeletal system among athletes and also in the older population. Investigation into ankle inversion requires quantitative assessment of the smallest amount of height/angle change in the floor that can be perceived by human. Blocks of different thickness have been used to change floor height manually during tests. We aimed to develop an automatic apparatus that is able to provide improved height and angle resolutions for dynamic ankle proprioception. We designed and manufactured a five-bar planar robot with one coupler serving as the mobile platform. We used a stiffening rib to achieve consistent differences in deflection across the workspace of the mobile platform. The reported robot translates at the maximal speed 423 mm/s with a resolution at 0.21 mm under a maximal load of 358 kg. This robot allows for increased sensitivity, which may lead to further investigation of functional proprioceptive ability and reflect finely tuned sensory requirements for upright stance.

Autonomous Switched Control of Load Shifting Robot Manipulators

An autonomous switched controller is proposed for a robot manipulator to ensure asymptotical tracking performance in the presence of shifting loads. Since the manipulator parameter jumps along with the load shift, the manipulator is modeled as a switched system whose switching signal depicts the load change. When the load change is not detectable, the switching signal of the switched system is unavailable. In this case, another “autonomous” switching law is designed for the given subcontrollers using a switched reset supervisory variable. Asymptotic tracking is ensured for slow load shifting. The effectiveness of the proposed method is verified by both simulations of a 2-DOF robot manipulator and experiments of a closed-chain five-bar robot.

Multidomain Model Integration for Online Collaborative System Design and Detailed Design of Complex Mechatronic Systems

Model integration enables the automated data flow of design changes between multidisciplinary models and improves the efficiency of collaborative multidisciplinary design. In this study, a geometry-related profile of SysML is first proposed to specify the geometric information of mechanical structure to support the generation of initial CAD models from SysML models. Second, a model comparison-based method is introduced to obtain the model changes to facilitate the incremental update between distributed heterogeneous models. Third, a multidisciplinary design optimization based method is introduced to deal with the design conflicts that hinder model integration during multidisciplinary design. Finally, a five-bar robot is used as a case study to demonstrate the whole proposed method.

Tracking Control of a Closed-Chain Five-Bar Robot With Two Degrees of Freedom by Integration of an Approximation-Based Approach and Mechanical Design

The trajectory tracking problem of a closed-chain five-bar robot is studied in this paper. Based on an error transformation function and the backstepping technique, an approximation-based tracking algorithm is proposed, which can guarantee the control performance of the robotic system in both the stable and transient phases. In particular, the overshoot, settling time, and final tracking error of the robotic system can be all adjusted by properly setting the parameters in the error transformation function. The radial basis function neural network (RBFNN) is used to compensate the complicated nonlinear terms in the closed-loop dynamics of the robotic system. The approximation error of the RBFNN is only required to be bounded, which simplifies the initial "trail-and-error" configuration of the neural network. Illustrative examples are given to verify the theoretical analysis and illustrate the effectiveness of the proposed algorithm. Finally, it is also shown that the proposed approximation-based controller can be simplified by a smart mechanical design of the closed-chain robot, which demonstrates the promise of the integrated design and control philosophy.