Force sensorless control with 3D workspace analysis for haptic devices based on delta robot

Recently, the human-robot interaction systems are extending their capability to work in many applications. Appropriate haptic devices or robot systems have an important role to support the collaboration task which require close interaction between human and robot. This paper proposes a novel 3D parallel mechanism delta robot based on Kalman filter based state observer for the haptic devices. In the mechanical design, a 3D parallel mechanism delta robot contains three identical and symmetrical upper-arm and fore-arm of parallel links that are connected to the base plate with motor at lower end and to the end-effector with a ball joint at top end. The delta robot are designed and analyzed in order to realize a 3D motion in the workspace of the robot's end-effector. In the controller design, Kalman filter based state observer, (KFSO) can be applied to estimate an external torque of motor. With such a force sensorless control scheme based on the feedforward disturbance torque compensation loop, significant high bandwidth haptic sensing in a delta robot system can be achieved. The proposed method has been successfully applied in a real delta robot system in order to show its applicability. The experimental results of the rehabilitation robot are provided to illustrate the performance of the proposed algorithms. In the experimental results, the system was able to estimate a 3D motion and external force in the workspace of the robot's end-effector.

[1]  Yuichi Matsumoto,et al.  Modeling of force sensing and validation of disturbance observer for force control , 2003, IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468).

[2]  Kiyoshi Ohishi,et al.  Modal System Design of Multirobot Systems by Interaction Mode Control , 2007, IEEE Transactions on Industrial Electronics.

[3]  S. Katsura,et al.  Modal system design of multi-robot systems by interaction mode control , 2006, 9th IEEE International Workshop on Advanced Motion Control, 2006..

[4]  Kevin C. Olds,et al.  Global Indices for Kinematic and Force Transmission Performance in Parallel Robots , 2015, IEEE Transactions on Robotics.

[5]  Aude Billard,et al.  Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction , 2014, IEEE Transactions on Haptics.

[6]  Xin Chen,et al.  Optimization Algorithms for Kinematically Optimal Design of Parallel Manipulators , 2014, IEEE Transactions on Automation Science and Engineering.

[7]  Ferdinando Cannella,et al.  Optimal Subtask Allocation for Human and Robot Collaboration Within Hybrid Assembly System , 2014, IEEE Transactions on Automation Science and Engineering.

[8]  Aude Billard,et al.  Hand Impedance Measurements During Interactive Manual Welding With a Robot , 2015, IEEE Transactions on Robotics.

[9]  Kiyoshi Ohishi,et al.  Kalman-Filter-Based Sensor Integration of Variable Power Assist Control Based on Human Stiffness Estimation , 2009, IEEE Transactions on Industrial Electronics.

[10]  Heikki Haario,et al.  Novel Online Re-Calibration Method for Multi-Axis Force/Torque Sensor of ITER Welding/Machining Robot , 2013, IEEE Sensors Journal.

[11]  Marcello Pellicciari,et al.  Energy Efficient Use of Multirobot Production Lines in the Automotive Industry: Detailed System Modeling and Optimization , 2014, IEEE Transactions on Automation Science and Engineering.