Low-precision System Research Articles

Tabular Open Circuit Voltage Modelling of Li-Ion Batteries for Robust SOC Estimation

Battery management systems depend on open circuit voltage (OCV) characterization for state of charge (SOC) estimation in real time. The traditional approach to OCV-SOC characterization involves collecting OCV-SOC data from sample battery cells and then fitting a polynomial model to this data. The parameters of these polynomial models are known as the OCV-parameters, or OCV-SOC parameters, in battery management systems and are used for real-time SOC estimation. Even though traditional OCV-SOC characterization approaches are able to abstract the OCV-SOC behavior of a battery in a few parameters, these parameters are only applicable in high precision computing systems. However, many practical battery applications do not have access to high-precision computing resources. The typical approach in a low-precision system is to round the OCV-parameters. This paper highlights the perils of rounding the OCV parameters and proposes an alternative OCV-SOC table. First, several existing OCV-SOC parameters are compared in terms of their expected system requirements and accuracy losses due to rounding. Then, a systematic optimization-based approach is introduced to create an OCV-SOC table that is robust to rounding. A formal performance evaluation metric is introduced to measure the robustness of the resulting OCV-SOC table. It is shown that the OCV-SOC table obtained through the proposed optimization approach outperforms the traditional parametric OCV-SOC models with respect to rounding.

Online modeling of environmental constraint region for complex-shaped parts assembly

Purpose The paper aims to propose a method to build environmental constraint region online in complex-shaped peg-in-hole assembly tasks. Design/methodology/approach Compared with conventional way which using computer-aided design (CAD) models of assembly parts to construct the environmental constraint region offline, the paper provides an online approach that consists of three aspects: modeling assembly parts through visual recognition, decomposing complex shapes into multiple primitive convex shapes and a numerical algorithm to simulate the peg-in-hole insertion contact. Besides, a contrast experiment is performed to validate the feasibility and effectiveness of the method. Findings The experiment result indicates that online construction takes less time than the offline way under the same task conditions. Furthermore, due to the CAD models of the parts are not required to be known, the method proposed in the paper has a broader application in most assembly scenarios. Originality/value With the improvement of customization and complexity of manufactured parts, the assembly of complex-shaped parts has drawn greater attention of many researchers. The assembly methods based on attractive region in environment (ARIE) have shown great performance to achieve high-precision manipulation with low-precision systems. The construction of environmental constraint region serves as an essential part of ARIE-based theory, directly affect the formulation and application of assembly strategies.

Condition and Strategy Analysis for Assembly Based on Attractive Region in Environment

Automatic assembly with high precision is an essential step in robotic manipulation. It is still a difficult problem due to various complicated requirements, such as less contact force, irregular shapes of the parts, sensing information understanding in complex environment, etc. In the previous work, the concept of attractive region in environment (ARIE) was proposed, which helps to achieve high-precision manipulation with low-precision systems by using the constraints formed by the environment. In this paper, the general relation between the physical space and the configuration space for the robotic assembly system will be analyzed, the strategies of using ARIE to achieve high-precision peg-hole assembly will be provided, and furthermore, practical examples for industrial applications will be given, which further illustrates the usefulness of the concept.

The Concept of “Attractive Region in Environment” and its Application in High-Precision Tasks With Low-Precision Systems

High-precision manipulation is very important for a variety of tasks in manufacturing. In general, high-precision manipulation is usually achieved by the guidance of high-precision sensors; however, sensorless methods, for some special cases, are more reliable and effective. There are many important works in this aspect [1] – [3] . The concept of “attractive region in environment” (ARIE), which was proposed in our previous works [4] – [6] , provides a prospective approach for low-precision systems to achieve high-precision manipulation. It has been used in various industrial applications, such as robotic assembly [7] , grasping [8] , and localization [9] . ARIE is a region from any point of which the uncertainty of the system can be eliminated by a state-independent input. The utilization of attractive region can be easily understood by the case of a bean in a bowl. For a bean in a bowl, no matter where the initial position of the bean is, it will move to and finally stay at the bottom of the bowl under gravity. Thus, the uncertainty of the bean is eliminated by gravity without any sensor feedback. Inspired by the above case, the general concept of ARIE is proposed and is applied to industry. In this paper, we review the application of ARIE and give its formal definition. Furthermore, we establish conditions for the existence of ARIE in the generalized configuration space, which is complex and nonideal. The relationship between the high-dimensional attractive region and the low-dimensional one is also discussed, which is helpful to make feasible and reliable manipulation strategies in the low-dimensional configuration space. The main contribution of this paper is the generalization of the concept of ARIE, which provides a theoretical foundation for applications including the high-precision manipulation with low-precision system. The experimental simulations on different kinds of complex manipulations show the effectiveness of the proposed method for industrial applications.