This article investigates modeling and calibration issues that are associated with inertially stabilized platforms to achieve accurate pointing. In modeling part, the Denavit-Hartenberg notation is used to perform an error analysis of the kinematics of inertially stabilized platforms. A physical model is then established to illustrate the effects of geometric errors that are caused by imprecision in the manufacturing and assembly processes on the pointing accuracy of inertially stabilized platforms. In the calibration part, an improved hybrid model denoted as the semi-parametric regression model is developed to compensate for remaining nonlinear errors. With applications to a two-degree-of-freedom miniature inertially stabilized platform, semi-parametric regression model is shown to outperform physical model substantially in all cases. The experimental results also indicate that the proposed semi-parametric regression model eliminates both the geometric and nonlinear errors, and that the pointing accuracy of miniature inertially stabilized platform significantly improves after compensation.
As a research hotspot in the field of artificial intelligence, the application of deep reinforcement learning to the learning of the motion ability of a manipulator can help to improve the learning of the motion ability of a manipulator without a kinematic model. To suppress the overestimation bias of values in Deep Deterministic Policy Gradient (DDPG) networks, the Twin Delayed Deep Deterministic Policy Gradient (TD3) was proposed. This paper further suppresses the overestimation bias of values for multi-degree of freedom (DOF) manipulator learning based on deep reinforcement learning. Twin Delayed Deep Deterministic Policy Gradient with Rebirth Mechanism (RTD3) was proposed. The experimental results show that RTD3 applied to multi degree freedom manipulators is in place,with an improved learning ability by 29.15% on the basis of TD3. In this paper, a step-by-step reward function is proposed specifically for the learning and innovation of the multi degree of freedom manipulator’s motion ability. The view of continuous decision-making and process problem is used to guide the learning of the manipulator, and the learning efficiency is improved by optimizing the playback of experience. In order to measure the point-to-point position motion ability of a manipulator, a new evaluation index based on the characteristics of the continuous decision process problem, energy efficiency distance, is presented in this paper, which can evaluate the learning quality of the manipulator motion ability by a more comprehensive and fair evaluation algorithm.
Research on the cable-driven mechanism has greatly developed with the booming of the robots in the past 30 years, and a range of corresponding theoretical studies have been published on them. The large-scale robot or manipulator with the complex cable-driven mechanism can be reconfigured. However, more theoretical studies are required on their topological architecture design and optimization to achieve this. Therefore, the applied cable-driven architectures and the corresponding theoretical studies are reviewed and summarized here. The parallel, serial, and differential architecture are illustrated, as well as their theories and methods, such as the workspace analysis based on the Jacobian matrix, particle swarm optimization and genetic algorithm, and kinematic design based on the graph theory are described. The features of the architecture and the theory studies are concluded. It is hoped that this study will help with design of future studies.
This paper presents a hybrid topology optimization method for multipatch fused deposition modeling (FDM) 3D printing to address the process-induced material anisotropy. The ‘multipatch’ concept consists of each printing layer disintegrated into multiple patches with different zigzag-type filament deposition directions. The level set method was employed to represent and track the layer shape evolution; discrete material optimization (DMO) model was adopted to realize the material property interpolation among the patches. With this set-up, a concurrent optimization problem was formulated to simultaneously optimize the topological structure of the printing layer, the multipatch distribution, and the corresponding deposition directions. An asynchronous starting strategy is proposed to prevent the local minimum solutions caused by the concurrent optimization scheme. Several numerical examples were investigated to verify the effectiveness of the proposed method, while satisfactory optimization results have been derived.
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