Redundant Posture Optimization for 6R Robotic Milling Based on Piecewise-Global-Optimization-Strategy Considering Stiffness, Singularity and Joint-Limit

Robotic machining has obtained growing attention recently because of the low cost, high flexibility and large workspace of industrial robots (IRs). Multiple degrees of freedom of IRs improve the dexterity of machining while causing the problem of redundancy. Meanwhile, the performance of IRs, such as their stiffness and dexterity, is affected by their position and posture obviously. Therefore, a redundant posture optimization method for robotic milling is proposed to improve the machining performance of the robot. The multiple characteristics of the robot are considered, including the joint-limit, singularity and stiffness, which have symmetry in its workspace. Firstly, the joint-limit is regarded as the constraint. And a symmetrical and effective constraint method is proposed to simply guarantee that all the interpolation points can avoid joint interference. Then, the performance indices of singularity and stiffness are designed as the optimization target. On this basis, the piecewise-global-optimization-strategy (PGOS) is proposed for redundant optimization. Owning to the PGOS, all the given planned tool points in their corresponding segment are considered simultaneously to avoid the gradual deterioration in traditional methods, which is especially suitable for the machining process with a continuous path. Moreover, the computational load of the optimization solution is considered and limited by the designed segmentation strategy. Finally, a series of comparative simulations are conducted to validate the good performance of the proposed method

Redundant Posture Optimization for 6R Robotic Milling Based on Piecewise-Global-Optimization-Strategy Considering Stiffness, Singularity and Joint-Limit

Robotic machining has obtained growing attention recently because of the low cost, high flexibility and large workspace of industrial robots (IRs). Multiple degrees of freedom of IRs improve the dexterity of machining while causing the problem of redundancy. Meanwhile, the performance of IRs, such as their stiffness and dexterity, is affected by their position and posture obviously. Therefore, a redundant posture optimization method for robotic milling is proposed to improve the machining performance of the robot. The multiple characteristics of the robot are considered, including the joint-limit, singularity and stiffness, which have symmetry in its workspace. Firstly, the joint-limit is regarded as the constraint. And a symmetrical and effective constraint method is proposed to simply guarantee that all the interpolation points can avoid joint interference. Then, the performance indices of singularity and stiffness are designed as the optimization target. On this basis, the piecewise-global-optimization-strategy (PGOS) is proposed for redundant optimization. Owning to the PGOS, all the given planned tool points in their corresponding segment are considered simultaneously to avoid the gradual deterioration in traditional methods, which is especially suitable for the machining process with a continuous path. Moreover, the computational load of the optimization solution is considered and limited by the designed segmentation strategy. Finally, a series of comparative simulations are conducted to validate the good performance of the proposed method

One-Shot Unsupervised Domain Adaptation for Object Detection

The existing unsupervised domain adaptation (UDA) methods require not only labeled source samples but also a large number of unlabeled target samples for domain adaptation. Collecting these target samples is generally time-consuming, which hinders the rapid deployment of these UDA methods in new domains. Besides, most of these UDA methods are developed for image classification. In this paper, we address a new problem called one-shot unsupervised domain adaptation for object detection, where only one unlabeled target sample is available. To the best of our knowledge, this is the first time this problem is investigated. To solve this problem, a one-shot feature alignment (OSFA) algorithm is proposed to align the low-level features of the source domain and the target domain. Specifically, the domain shift is reduced by aligning the average activation of the feature maps in the lower layer of CNN. The proposed OSFA is evaluated under two scenarios: adapting from clear weather to foggy weather; adapting from synthetic images to real-world images. Experimental results show that the proposed OSFA can significantly improve the object detection performance in target domain compared to the baseline model without domain adaptation

In-Sensor Visual Perception and Inference

Conventional machine vision systems have separate perception, memory, and processing architectures, which may exacerbate the increasing need for ultrahigh image processing rates and ultralow power consumption. In contrast, in-sensor visual computing performs signal processing at the pixel level using the collected analog signals directly, without sending data to other processors. Therefore, the in-sensor computing paradigm may hold the key to realizing extremely efficient and low power visual signal processing by integrating sensing, storage, and computation onto focal planes using either novel circuit designs or new materials. The focal-plane sensor-processor (FPSP), which is a typical in-sensor visual computing device, is a vision chip that has been developed for nearly 2 decades in domains such as image processing, computer vision, robotics, and neural networks. In contrast to conventional computer vision systems, the FPSP gives vision systems in-sensor image processing capabilities, thus decreasing system complexity, reducing power consumption, and enhancing information processing efficiency and security. Although many studies on in-sensor computing using the FPSP have been conducted since its invention, no thorough and systematic summary of these studies exists. This review explains the use of image processing algorithms, neural networks, and applications of in-sensor computing in the fields of machine vision and robotics. The objective is to assist future developers, researchers, and users of unconventional visual sensors in understanding in-sensor computing and associated applications