Control of Adaptive Switching in the Sensing-Executing Mode Used to Mitigate Collision in Robot Force Control

Mitigating collision is a fundamental issue in contact problems, and is required to ensure the safety of a robotic cell. Research into the contact problem between robots and their environment is divided into two parts: one uses the environmental contact model and parameter estimation, the other uses the robot force control method. There are two main problems with this research method. One is that the two research levels are not effectively combined to form a complete solution for force control in practice. The other problem is that research on excessive contact force in the collision phase has not been studied in depth for force control. In this paper, a sensing-executing bionic system is proposed that combines environmental detection and robotic force control based on the way an ant functions. The bionic system clearly explains the process from environment detection to robot control, which can provide guidance when designing a new robot control system. An adaptive switching control algorithm is proposed to mitigate the collision force in the collision phase. From the simulation results, the collision force is significantly reduced due to the implementation of adaptive switching control. Finally, a new self-sensing device is designed which can be integrated into the robot control device. However, as there are no additional sensors or computational complexity in the system, the effectiveness of the circuit and superiority of the adaptive parameter update must be verified by experimentation.

[1]  Giorgio Grioli,et al.  Variable Stiffness Actuators: Review on Design and Components , 2016, IEEE/ASME Transactions on Mechatronics.

[2]  Moonhong Baeg,et al.  Compliance-Based Robotic Peg-in-Hole Assembly Strategy Without Force Feedback , 2017, IEEE Transactions on Industrial Electronics.

[3]  Nikos A. Aspragathos,et al.  Variable admittance control in pHRI using EMG-based arm muscles co-activation , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[4]  John J. Craig,et al.  Hybrid position/force control of manipulators , 1981 .

[5]  Marina Indri,et al.  Development of a Virtual Collision Sensor for Industrial Robots , 2017, Sensors.

[6]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation: Part I—Theory , 1985 .

[7]  Inna Sharf,et al.  Contact Stiffness and Damping Estimation for Robotic Systems , 2003, Int. J. Robotics Res..

[8]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation: Part III—Applications , 1985 .

[9]  Daniel Liberzon,et al.  Switching in Systems and Control , 2003, Systems & Control: Foundations & Applications.

[10]  Arno H. A. Stienen,et al.  Admittance control for physical human–robot interaction , 2018, Int. J. Robotics Res..

[11]  Matthew T. Mason,et al.  Compliance and Force Control for Computer Controlled Manipulators , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  Beno Benhabib,et al.  Robot Imitation Learning of Social Gestures with Self-Collision Avoidance Using a 3D Sensor , 2018, Sensors.

[13]  Christian Ott,et al.  Unified Impedance and Admittance Control , 2010, 2010 IEEE International Conference on Robotics and Automation.

[14]  Sami Haddadin,et al.  Collision detection, isolation and identification for humanoids , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Simeon P. Patarinski,et al.  Robot force control: A review , 1993 .

[16]  Max Q.-H. Meng,et al.  Impedance control with adaptation for robotic manipulations , 1991, IEEE Trans. Robotics Autom..

[17]  Yoshihiko Nakamura,et al.  A Hybrid System Framework for Unified Impedance and Admittance Control , 2015, J. Intell. Robotic Syst..

[18]  Mauro Massari,et al.  Adaptive Hybrid System Framework for Unified Impedance and Admittance Control , 2018, J. Intell. Robotic Syst..

[19]  Stefano Stramigioli,et al.  Contact impedance estimation for robotic systems , 2005, IEEE Trans. Robotics.

[20]  Yahui Gan,et al.  Adaptive variable impedance control for dynamic contact force tracking in uncertain environment , 2018, Robotics Auton. Syst..

[21]  Manuel G. Catalano,et al.  Variable impedance actuators: A review , 2013, Robotics Auton. Syst..

[22]  Wayne J. Book,et al.  Environment estimation for enhanced impedance control , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[23]  Dale A. Lawrence,et al.  Impedance control stability properties in common implementations , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[24]  G. Oriolo,et al.  Robotics: Modelling, Planning and Control , 2008 .

[25]  Chee Khiang Pang,et al.  Robust vibration control at critical resonant modes using indirect-driven self-sensing actuation in mechatronic systems. , 2012, ISA transactions.

[26]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation: Part II—Implementation , 1985 .

[27]  Jean-Jacques E. Slotine,et al.  Adaptive manipulator control: A case study , 1988 .