A new iterative spiking adaptive dynamic programming (SADP) algorithm based on the Poisson process for optimal impulsive control problems is investigated with convergence discussion of the iterative process. For a fix...
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Operating in narrow spaces is an important challenge in the development of robots. Redundant manipulators are one way to solve this problem, but their mechanism design and control method still have much room for impro...
Operating in narrow spaces is an important challenge in the development of robots. Redundant manipulators are one way to solve this problem, but their mechanism design and control method still have much room for improvement. In this paper, we propose a coiled cable-conduit-driven hyper-redundant manipulator (C-CDHRM) with great slenderness and flexibility. In terms of mechanism design, it considers both compactness and operability. By imitating the structure and behavior of a constricting snake, it can be uncoiled sequentially from a coiled storage state, led by the head. In terms of control methods, we propose a multi-layer control system that can make remote operations more accurate and reliable. On the one hand, guiding, segmenting, and following the path overcome the planning ambiguity caused by redundancy. On the other hand, conduit transmission modeling and cable length correction overcome the nonlinear mapping of cable-driven joints and were verified in experiments. Through tests, the mobile integrated system composed of C-CDHRM has an excellent performance in operation precision and accuracy, ensuring safety and accessibility in narrow spaces. Finally, in field experiments, the inspection and cleaning of various types of electrical equipment have been successfully completed, showing excellent application prospects.
This paper proposes a hand position tracking algorithm based on optimized consistent extended Kalman filter(CEKF).By introducing the previous work of the authors and analyzing the parameter of the original CEKF algori...
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This paper proposes a hand position tracking algorithm based on optimized consistent extended Kalman filter(CEKF).By introducing the previous work of the authors and analyzing the parameter of the original CEKF algorithm,the key parameter that is negatively correlated with the degree of the estimation of uncertain dynamics is ***,two metaheuristic methods,the Genetic Algorithm(GA) and the Particle Swarm Optimization(PSO) are used to optimize the original CEKF *** quantify the performance of the algorithms,the root-mean-square error(RMSE) is employed as the performance ***,the numerical simulation and practical experiment of the hand position tracking are carried out,and the optimized algorithm achieves 9.52% and 10.94% improvements of the performance,respectively.
Aiming at a large number of ambiguous,imprecise and incomplete data in the real world,fuzzy time series has come into being and developed into an effective forecasting *** the process of modeling and forecasting of fu...
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Aiming at a large number of ambiguous,imprecise and incomplete data in the real world,fuzzy time series has come into being and developed into an effective forecasting *** the process of modeling and forecasting of fuzzy time series,the prediction performance of fuzzy time series can be effectively improved by partitioning the universe of discourse into different *** this paper,a forecasting approach for fuzzy time series,which introduces the granularity mechanism into interval division and employs differential data for incremental forecasting,is proposed to solve the problem of time series forecasting with high forecasting *** the proposed approach,in order to describe the fuzzy logic relationship and fuzzy trend of historical data,we first do differential processing on the historical ***,Fuzzy C-means(FCM) clustering algorithm is used to generate several partition intervals *** the sequel,we use the principle of justifiable granularity to constantly adjust the width of all the intervals,so that these information granules associated with corresponding intervals become the most"informative" information ***,the boundary of information granules is used as the basis of interval division to complete the forecasting *** illustrative example is provided to demonstrate the essence of the proposed *** comparative experiment with other representative approaches shows that the proposed approach can significantly improve the prediction accuracy of time series.
This paper investigates the high-precision path tracking control of tracked paver combined with global satellite navigation *** the paver is performing paving operations,it requires high path tracking accuracy and goo...
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This paper investigates the high-precision path tracking control of tracked paver combined with global satellite navigation *** the paver is performing paving operations,it requires high path tracking accuracy and good vehicle ***,considering the influence of road curvature on path tracking accuracy and vehicle stability,and the situation that the vehicle can not move quickly to the expectation path when the lateral position of the vehicle deviates from the expectation path,this paper proposes a lateral path tracking control method based on improved Pure Pursuit *** control method is verified through *** experimental results show that the maximum lateral tracking error of the improved algorithm is 0.04 m,which is 55.56% lower than that of the original algorithm,and the average lateral tracking error is 0.02 m,which is60% lower than that of the original *** purpose of high-precision path tracking of the paver is realized.
Aiming at the problem of how to realize the reasonable distribution of goods and the resource scheduling of route planning under dynamic conditions, a phased refresh method is proposed to deal with the requirements in...
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Aiming at the problem of how to realize the reasonable distribution of goods and the resource scheduling of route planning under dynamic conditions, a phased refresh method is proposed to deal with the requirements in sections. At the same time, the clustering method is introduced to generate the initial population. And the cross and mutation rate is determined by a step-by-step reduction method, which is used to solve the problem of the traditional genetic algorithm falling into the local optimum caused by the uneven distribution of searchable solutions. Finally, the effectiveness of the improved method is verified by simulation experiments.
Adaptive dynamic programming(ADP) is a kind of intelligent control method,and it is a non-model-based method that can directly approximate the optimal control policy via online *** gradient algorithm is usually used t...
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Adaptive dynamic programming(ADP) is a kind of intelligent control method,and it is a non-model-based method that can directly approximate the optimal control policy via online *** gradient algorithm is usually used to update weights of action networks and critic networks,however it is clear that gradient descent-based learning methods are generally very slow due to improper learning steps or may easily converge to local *** this paper,in order to overcome those disadvantages of gradient descent-based learning methods,a novel ADP algorithm based on initial-training-free online extreme learning machine(ITF-OELM),in which the critic network link weights of hidden nodes to output nodes can be obtained by least squares instead of gradient algorithm,is ***,the ADP algorithm based on ITF-OELM is tested on a discrete time torsional pendulum system,and simulation results indicate that this algorithm makes the system converge in a shorter time compared with the ADP based on gradient algorithm.
In the modern manufacturing environment, the production systems develop towards a flexible direction due to the market demands of multiple varieties and small batch-based customized products. To make better use of the...
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In the modern manufacturing environment, the production systems develop towards a flexible direction due to the market demands of multiple varieties and small batch-based customized products. To make better use of the existing resources and to improve production efficiency, distributed manufacturing mode begins to appear. This paper concentrates on a multi-type production scheduling problem for distributed serial lines. Specifically, unreliable machines and finite buffers are considered,which are more realistic in the practical production. The mathematical model for the production system with two Bernoulli machines is formulated first. Then, under the hypothetical model mentioned above, the main work of this article is to propose the exact and approximate analyses to calculate the performance indicators. Based on the analysis of the model, the genetic algorithm is applied to optimize the maximum completion time on the distributed serial lines for multi-batch-based production tasks. Finally, the feasibility and effectiveness of the mathematical model and the proposed algorithm are verified through numerical experiments.
This paper presents a weakly supervised object detection method based on activity label and relationship modeling, which is motivated by the assumption that configuration of human and object are similar in same activi...
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This paper presents a weakly supervised object detection method based on activity label and relationship modeling, which is motivated by the assumption that configuration of human and object are similar in same activity, and joint modeling of human, active object and activity could leverage the recognition of them. Compared to most weakly supervised method taking object as independent instance, firstly, active human and object proposals are learned and filtered based on class activation map of multi-label classification. Secondly, a spatial relationship prior including relative position, scale, overlaps etc are learned dependent on action. Finally, a multi-stream object detection framework integrating the spatial prior and pairwise ROI pooling are proposed to jointly learn the object and action class. Experiments are conducted on HICO-DET dataset, and our approach outperforms the state of the art weakly supervised object detection methods.
Different related computer vision tasks make it possible to understand and analyze the scene totally. However, there is no useful framework combining different tasks for scene understanding. In this paper, we investig...
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