In this paper, we present a trajectory generation method of a quadrotor, based on the optimal smoothing B-spline, for tracking a moving target with consideration of relative tracking pattern or limited field of view o...
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In this paper, we present a trajectory generation method of a quadrotor, based on the optimal smoothing B-spline, for tracking a moving target with consideration of relative tracking pattern or limited field of view of the onboard sensor in cluttered environments. Compared to existing methods, safe flying zone,vehicle physical limits, and smoothness are fully considered to guarantee flight safety, kinodynamic feasibility, and tracking performance. To tackle the cluttered environments, a parallel particle swarm optimization algorithm is applied to find the feasible waypoints that the generated trajectory should be as close to as possible, with consideration of the target's future state as well as obstacles to trade off the tracking performance and flight safety. Then, a sequential motion planning method, considering the above constraints, is applied and embedded into a cost function for solving the problem of robust tracking trajectory generation for the quadrotor via a convex optimization approach. The feasibility and effectiveness of the proposed method are verified by numerical simulations.
The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence,internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to t...
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The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence,internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems,QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called Eu Artisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.
The agent routing problem in multi-point dynamic task (ARP-MPDT) proposed recently is a novel permutation optimisation problem. In ARP-MPDT, a number of task points are located at different places and their states cha...
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In this paper,a novel particle swarm algorithm for solving constrained multiobjective optimization problems is *** new algorithm is able to utilize valuable information from the infeasible region by intentionally keep...
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ISBN:
(纸本)9781479947249
In this paper,a novel particle swarm algorithm for solving constrained multiobjective optimization problems is *** new algorithm is able to utilize valuable information from the infeasible region by intentionally keeping a set of infeasible solutions in each *** enhance the diversity of these preserved infeasible solutions,a modified version of adaptive grid is *** addition,a voting mechanism is designed to balance the preference of infeasible solutions with smaller constraint violation and the exploration of the infeasible *** effectiveness of the proposed method is validated by simulations on several commonly used benchmark *** using the hypervolume indicator,it is shown that the proposed algorithm is more powerful than two other state-of-the-art algorithms.
With the rapid development of machine vision, many technologies have been applied to the robots for improving the efficiency in the industrial field. This paper concerns the industrial sorting and counting technology ...
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With the rapid development of machine vision, many technologies have been applied to the robots for improving the efficiency in the industrial field. This paper concerns the industrial sorting and counting technology problems in a workpieces counting and sorting system, and puts forward a solution using monocular vision. The main process consists of three parts. The rough positioning is accomplished first by using the pixel intensity comparison-based object detection(PICO). Then, image preprocessing and extracting geometric features are established, composing of binarization, morphological operation, optimizing the foreground,finding inner as well as outer contours, and calculating areas. Finally, the center coordinates and categories of workpieces are obtained. We choose nuts and gears as experimental objects, and complete the fast detection. The results of counting nuts and locating gears illustrate that the proposal solution not only has high speed, but also can ensure a high accuracy.
With the development of aeronautics and astronautics, the response speed of servo system need be faster. First, In order to improve the dynamic quality of servo system, the exponential and power reaching law, which co...
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With the development of aeronautics and astronautics, the response speed of servo system need be faster. First, In order to improve the dynamic quality of servo system, the exponential and power reaching law, which combines the advantages of the exponential reaching law and the power reaching law, is introduced. Second, the chattering of the sliding mode controller(SMC) with the exponential and power reaching law for discrete systems is investigated. Finally, the adaptive sliding mode controller(ASMC) with the exponential and power reaching law is introduced. The stability of the ASMC with the exponential and power reaching law for discrete systems is analyzed, and the simulation of this approach on one joint of a six degrees of freedom robot is carried out. The experimental results indicate that the ASMC with the exponential and power reaching law is effective in reducing the time of arriving the sliding mode surface. The experimental results also indicate that the ASMC with the exponential and power reaching law may make output error reach zero in a shorter time.
Although skeleton-based gesture recognition based on supervised learning has made promising achievements, the reliance on large amounts of annotation for training poses a significant cost. This paper addresses semi-su...
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This paper addresses the tuning problem of a proportional-integral-derivative(PID) controller with notch filter for flexible space structure model based on the particle swarm optimization (PSO). For flexible structure...
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Recently deep learning based architectures have been widely deployed in many problems of artificial *** deep learning models, Convolutional Neural Networks(CNN) have been reported in numerous successful applications...
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Recently deep learning based architectures have been widely deployed in many problems of artificial *** deep learning models, Convolutional Neural Networks(CNN) have been reported in numerous successful applications such as object recognition, and natural language processing. The convolutional neural networks are trained by back-propagating the classification error using the Back-Propagation(BP) algorithm, which requires a large amount of data and slows the training process. To overcome these difficulties, a new fast and accurate approach based on Extreme Learning Machine(ELM) to train any convolutional neural network has been proposed. The developed framework(ELM-CNN) is based on the concept of autoencoding to learn the convolutional filters with biases, by reconstructing the normalized input and the intercept term. In this paper, systematic comparison with traditional back-propagation based training method(BP-CNN) has been made with respect to two aspects qualitative and quantitative. The experimental results on the popular MNIST dataset show that the ELM-CNN algorithm achieves competitive results in terms of generalization performance and up to 16 times faster than the back-propagation based training of CNN.
Nowadays, more and more researchers pay attention to scene perception of artificial robot. Video visual relation detection is an essential task for scene perception but existing methods are all offline methods which a...
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