Xuanju Dang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xuanju Dang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Mingzhen Gui
School of Automation, Central South University, Changsha 410083, China
1 shared publication - Shanlin Sun
Affiliation School of Aeronautics and Astronautics, Guilin University of Aerospace Technology, Guilin, China
1 shared publication - Shike Long
Guangxi Key Laboratory of Low-Altitude Unmanned Autonomous System, Guilin University of Aerospace Technology, Guilin 541004, China
1 shared publication - Yongjun Wang
Guangxi Key Laboratory of Low-Altitude Unmanned Autonomous System, Guilin University of Aerospace Technology, Guilin 541004, China
1 shared publication
Latest Publications
- Open Access
A Novel Sliding Mode Momentum Observer for Collaborative Robot Collision Detection
Safety during physical human–robot interaction is the most basic requirement for robots. Collision detection without additional sensors is an economically feasible way to ensure it. In contrast, current collision detection approaches have an unavoidable trade-off between sensitivity to collisions, signal smoothness, and immunity to measurement noise. In this paper, we present a novel sliding mode momentum observer (NSOMO) for detecting collisions between robots and humans, including dynamic and quasistatic collisions. The collision detection method starts with a dynamic model of the robot and derives a generalized momentum-based state equation. Then a new reaching law is devised, based on which NSOMO is constructed by fusing momentum, achieving higher bandwidth and noise immunity of observation. Finally, a time-varying dynamic threshold (TVDT) model is designed to distinguish between collision signals and the estimated lumped disturbance. Its coefficients are obtained through offline data recognition. The TVDT with NSOMO enables fast and reliable collision detection and allows collision position assessment. Simulation experiments and hardware tests of the 7-DOF collaborative robot are implemented to illustrate this proposed method’s effectiveness.
Open AccessA Novel Sliding Mode Momentum Observer for Collaborative Robot Collision Detection
Safety during physical human–robot interaction is the most basic requirement for robots. Collision detection without additional sensors is an economically feasible way to ensure it. In contrast, current collision detection approaches have an unavoidable trade-off between sensitivity to collisions, signal smoothness, and immunity to measurement noise. In this paper, we present a novel sliding mode momentum observer (NSOMO) for detecting collisions between robots and humans, including dynamic and quasistatic collisions. The collision detection method starts with a dynamic model of the robot and derives a generalized momentum-based state equation. Then a new reaching law is devised, based on which NSOMO is constructed by fusing momentum, achieving higher bandwidth and noise immunity of observation. Finally, a time-varying dynamic threshold (TVDT) model is designed to distinguish between collision signals and the estimated lumped disturbance. Its coefficients are obtained through offline data recognition. The TVDT with NSOMO enables fast and reliable collision detection and allows collision position assessment. Simulation experiments and hardware tests of the 7-DOF collaborative robot are implemented to illustrate this proposed method’s effectiveness.