In the traditional sculpture surface machining process, the G01 code is still the mainstream trajectory. Furthermore, real-time feedrate scheduling and corner smooth algorithm in controller constitute the mainstream method to improve the machining process of short line G01 code in sculpture surface machining. However, the G01 code’s discontinuity and the limits of real-time calculation capacity hinder the use of high-speed machine tools and the accuracy of the machined part. In this article, a new method for sculpture surface machining that considers the advantages and disadvantages of both the computer-aided manufacturing software and the real-time controller is presented to promote the use of a continuous curve tool path. The method mainly transfers the computing-intensive feedrate scheduling and trajectory optimization algorithm in the real-time controller to the computer-aided manufacturing software. Furthermore, the computer-aided manufacturing software generates the machining data, which contain the geometry and feedrate information of the machining process. Finally, the real-time interpolator and the mathematical form of computer-aided manufacturing–generated data are designed simultaneously. In the method, the real-time controller can be designed as simple as possible to release more computing resources to the other real-time intelligent modules. The powerful computational capacity of the software guarantees the optimality of the machining process.
Event-driven control scheduling strategies for multiagent systems play a key role in future use of embedded microprocessors of limited resources that gather information and actuate the agent control updates. In this paper, a distributed event-driven consensus problem is considered for a multi-agent system with second-order dynamics. Firstly, two kinds of event-driven control laws are, respectively, designed for both leaderless and leader-follower systems. Then, the input-to-state stability of the closed-loop multi-agent system with the proposed event-driven consensus control is analyzed and the bound of the inter-event times is ensured. Finally, some numerical examples are presented to validate the proposed event-driven consensus control.
In an effort to implement fast and effective tank segmentation from infrared images in complex background, the threshold of the maximum between-class variance method (i.e., the Otsu method) is analyzed and the working mechanism of the Otsu method is discussed. Subsequently, a fast and effective method for tank segmentation from infrared images in complex background is proposed based on the Otsu method via constraining the complex background of the image. Considering the complexity of background, the original image is firstly divided into three classes of target region, middle background and lower background via maximizing the sum of their between-class variances. Then, the unsupervised background constraint is implemented based on the within-class variance of target region and hence the original image can be simplified. Finally, the Otsu method is applied to simplified image for threshold selection. Experimental results on a variety of tank infrared images (880 × 480 pixels) in complex background demonstrate that the proposed method enjoys better segmentation performance and even could be comparative with the manual segmentation in segmented results. In addition, its average running time is only 9.22 ms, implying the new method with good performance in real time processing.
This paper is concerned with the problem of estimating the relative orientation between an inertial measurement unit (IMU) and a camera. Unlike most existing IMU-camera calibrations, the main challenge in this paper is that the information output from the IMU is incomplete. For example, only two tilt information can be read from the gravity sensor of a smart phone. Despite incomplete inertial information, there are strong restrictions between the IMU and camera coordinate systems. This paper addresses the incomplete information based IMUcamera calibration problem by exploiting the intrinsic restrictions among the coordinate transformations. First, the IMU transformation between two poses is formulated with the unknown IMU information. Then the defective IMU information is restored using the complementary visual information. Finally, the Levenberg-Marquardt (LM) algorithm is applied to estimate the optimal calibration result in noisy environments. Experiments on both synthetic and real data show the validity and robustness of our algorithm.
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