Li Qin

Bio: Li Qin is an academic researcher from Yanshan University. The author has contributed to research in topics: Trajectory & Exoskeleton. The author has co-authored 1 publications.

Multi-Sensor Perception Strategy to Enhance Autonomy of Robotic Operation for Uncertain Peg-in-Hole Task

Abstract: The peg-in-hole task with object feature uncertain is a typical case of robotic operation in the real-world unstructured environment It is nontrivial to realize object perception and operational decisions autonomously, under the usual visual occlusion and real-time constraints of such tasks In this paper, a Bayesian networks-based strategy is presented in order to seamlessly combine multiple heterogeneous senses data like humans In the proposed strategy, an interactive exploration method implemented by hybrid Monte Carlo sampling algorithms and particle filtering is designed to identify the features' estimated starting value, and the memory adjustment method and the inertial thinking method are introduced to correct the target position and shape features of the object respectively Based on the Dempster-Shafer evidence theory (D-S theory), a fusion decision strategy is designed using probabilistic models of forces and positions, which guided the robot motion after each acquisition of the estimated features of the object It also enables the robot to judge whether the desired operation target is achieved or the feature estimate needs to be updated Meanwhile, the pliability model is introduced into repeatedly perform exploration, planning and execution steps to reduce interaction forces, the number of exploration The effectiveness of the strategy is validated in simulations and in a physical robot task

A Self-Coordinating Controller with Balance-Guiding Ability for Lower-Limb Rehabilitation Exoskeleton Robot

Abstract: The restricted posture and unrestricted compliance brought by the controller during human–exoskeleton interaction (HEI) can cause patients to lose balance or even fall. In this article, a self-coordinated velocity vector (SCVV) double-layer controller with balance-guiding ability was developed for a lower-limb rehabilitation exoskeleton robot (LLRER). In the outer loop, an adaptive trajectory generator that follows the gait cycle was devised to generate a harmonious hip–knee reference trajectory on the non-time-varying (NTV) phase space. In the inner loop, velocity control was adopted. By searching the minimum L2 norm between the reference phase trajectory and the current configuration, the desired velocity vectors in which encouraged and corrected effects can be self-coordinated according to the L2 norm were obtained. In addition, the controller was simulated using an electromechanical coupling model, and relevant experiments were carried out with a self-developed exoskeleton device. Both simulations and experiments validated the effectiveness of the controller.

Robotic Manipulation Planning for Automatic Peeling of Glass Substrate Based on Online Learning Model Predictive Path Integral

Abstract: Autonomous planning robotic contact-rich manipulation has long been a challenging problem. Automatic peeling of glass substrates of LCD flat panel displays is a typical contact-rich manipulation task, which requires extremely high safe handling through the manipulation process. To this end of peeling glass substrates automatically, the system model is established from data and is used for the online planning of the robot motion in this paper. A simulation environment is designed to pretrain the process model with deep learning-based neural network structure to avoid expensive and time-consuming collection of real-time data. Then, an online learning algorithm is introduced to tune the pretrained model according to the real-time data from the peeling process experiments to cover the uncertainties of the real process. Finally, an Online Learning Model Predictive Path Integral (OL-MPPI) algorithm is proposed for the optimal trajectory planning of the robot. The performance of our algorithm was validated through glass substrate peeling tasks of experiments.