Xiaoqing Li

3D vision based fast badminton localization with prediction and error elimination for badminton robot

In this paper, the problem of fast badminton localization problem is investigated for a class of badminton robots. More precisely, a manifold-learning based localization method is implemented for the improvement of hitting accuracy and effectiveness. Based on the localization results, a novel badminton trajectory prediction algorithm is designed based on 3D Vision in the real world. Furthermore, clock-synchronization combined with motion compensation methods are also proposed to better localization error elimination. In the end, the validity and usefulness of our proposed algorithm is demonstrated by numerical experiments.

A Unified Model for Solving the OOV Problem of Chinese Word Segmentation

This article proposes a unified, character-based, generative model to incorporate additional resources for solving the out-of-vocabulary (OOV) problem of Chinese word segmentation, within which different types of additional information can be utilized independently in corresponding submodels. This article mainly addresses the following three types of OOV: unseen dictionary words, named entities, and suffix-derived words, none of which are handled well by current approaches. The results show that our approach can effectively improve the performance of the first two types with positive interaction in F-score. Additionally, we also analyze reason that suffix information is not helpful. After integrating the proposed generative model with the corresponding discriminative approach, our evaluation on various corpora---including SIGHAN-2005, CIPS-SIGHAN-2010, and the Chinese Treebank (CTB)---shows that our integrated approach achieves the best performance reported in the literature on all testing sets when additional information and resources are allowed.

Human-inspired compliant strategy for peg-in-hole assembly using environmental constraint and coarse force information

Automated assembly, especially peg-in-hole insertion, is a common task in manufacturing. In particular, the high-precision assembly is achieved by high-precision manipulator and sensing system. However, uncertainty and various parts for assembly are still challenges for robotic assembly, especially for low-precision robot and sensors. It is noteworthy that human can implement assembly tasks although the precision of the arm and hand is not comparable with a common industrial robot, in which process compliance is the key characteristic of their motion. In this paper, we present a human-inspired compliant strategy for peg-in-hole assembly task using the environmental constraint and coarse force information. In the proposed strategy, a constraint region is designed for motion planning and utilized for eliminating the uncertainty of the initial positioning error of the peg. Force sensor is applied to sense the contact force of which the direction is used to adjust the movement of the peg. Therefore, high-precision sensor is not necessarily required. Inspired by human compliant assembly, a from coarse to fine adjustment strategy is executed. The contribution of our strategy is that high precision assembly task can be solved by low precision system. The constraint region and force guided directional adjustment have increased the robustness of the system. The strategy is carried out in simulation for round peg-in-hole assembly task. The experimental results show that the assembly task can be successfully completed and demonstrate the effectiveness of our strategy.

Simple coarse sensing to achieve high precision From attractive region in environment to constrained region in environment

ABSTRACT As a fundamental yet urgent issue, achieving high-precision robotic manipulation is becoming an active research area. Unfortunately, it is practically impossible to obtain an unlimited increase in sensor accuracy. In contrast, sensor-less manipulation approaches have attracted considerable attention. In particular, the concept of attractive region in environment has achieved encouraging results. Moreover, note that certain information cannot be captured directly from the sensors. With the above observations and inspired by the behaviour mechanism of human beings when accomplishing complex operations, this article proposed a novel bio-inspired co-sensing framework, which aims to solve the high-precision sensing problem by simple coarse sensors. One of the distinguishing features of the established framework is the constrained region in environment, the key idea of which is the integration of the relaxed constraint and the coarse sensor information. An illustrative example is provided to demonstrate the effectiveness and benefits of our theoretical findings.

A dynamical compliant grasping strategy for dexterous robotic hands with cushioning mechanism

This paper investigates the dynamical compliant grasping problem for a class of dexterous robotic hands. In particular, a novel bio-inspired cushioning mechanism is introduced, which can guarantee the compliant achievement of grasping the moving objects. With visual information of the moving objects, the grasping strategy can be implemented for dexterous robotic hands with two specific space regions at different stages. Compared with traditional grasping methods, significant advantages can be obtained such as grasping feasibility, stability and flexibility. Finally, the experimental example is provided to demonstrate the effectiveness and advantages of our obtained theoretical results.