Abstract
This article presents a novel model-based sensorless collision detection scheme for human–robot interaction. In order to recognize external impacts exerted on the manipulator with sensitivity and robustness without additional exteroceptive sensors, the method based on torque residual, which is the difference between nominal and actual torque, is adopted using only motor-side information. In contrast to classic dynamics identification procedure which requires complicated symbolic derivation, a sequential dynamics identification was proposed by decomposing robot dynamics into gravity and friction item, which is simple in symbolic expression and easy to identify with least squares method, and the remaining structure-complex torque effect. Subsequently, the remaining torque effect was reformulated to overcome the structural complexity of original expression and experimentally recovered using a machine learning approach named Lasso while keeping the involving candidates number reduced to a certain degree. Moreover, a state-dependent dynamic threshold was developed to handle the abnormal peaks in residual due to model uncertainties. The effectiveness of the proposed method was experimentally validated on a conventional industrial manipulator, which illustrates the feasibility and simplicity of the collision detection method.
Highlights
Compared to conventional robots strictly confined within safe fence to avoid any possibility of physical interaction when robot is performing a predefined task, collaborative robots, known as co-bots, have been regarded as a new generation of robots with the feasibility to complete the complementary task with close involvement of operator or direct physical contact with human for cooperation through high-level autonomy
A current-based whole body collision detection is presented on a conventional industrial manipulator based on a quick dynamic model identification using Lasso and a residual evaluation method to overcome the dynamic model uncertainties
A sequential identification of dynamic parameters is introduced where the major components of the dynamics in pHRI scenario are identified in the first step and the remaining torque is formulated with a simplified model
Summary
Robot collaborating with humans in both domestic and industrial area is increasingly becoming a research focus, and much effort has been devoted to realize human–robot collaboration in which both counterparts share the same workspace to accomplish a goal via different interaction modes.[1,2] Compared to conventional robots strictly confined within safe fence to avoid any possibility of physical interaction when robot is performing a predefined task, collaborative robots, known as co-bots, have been regarded as a new generation of robots with the feasibility to complete the complementary task with close involvement of operator or direct physical contact with human for cooperation through high-level autonomy. Given an observation of robot joint motion q and generalized joint forces/torques t for N samples at sampling time tk, the dynamic parameter vector p may be determined by least square regression as p^
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
