Abstract This paper addresses the problem of distributed connectivity constrained motion coordination of multiple autonomous mobile agents. Different from traditional flat network structure which lacks flexibility and...
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Abstract This paper addresses the problem of distributed connectivity constrained motion coordination of multiple autonomous mobile agents. Different from traditional flat network structure which lacks flexibility and scalability when performing complex spatially distributed tasks, a novel distributed framework for construction of backbone-based hierarchical communication networks is proposed. Firstly, the proposed method periodically extracts a subset of agents which can form the communication backbone from the original network using only local information, thus partitions the system into backbone agents and non-backbone agents. Furthermore, the global network connectivity of the system is maintained at two levels: connectivity-preserving potential functions are used to maintain existing links in the backbone; connectivity between backbone and non-backbone agents is achieved via a leader-follower formation control scheme with backbone agents as the leaders. Finally, nontrivial numerical simulations are worked out to verify the theoretical results.
An efficient evaluation index system and evaluation model is crucial for effectiveness evaluation of the air defense networked fire controlsystem. This paper, based on pre-research for functions and characteristics o...
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In this paper, task allocation of multi-Unmanned Aerial Vehicles (UAVs) is studied, that is, multi-UAVs from different bases should be allocated to attack multiple targets. Based on the existing task allocation model,...
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In this paper, task allocation of multi-Unmanned Aerial Vehicles (UAVs) is studied, that is, multi-UAVs from different bases should be allocated to attack multiple targets. Based on the existing task allocation model, which just take the values of targets, UAVs and weapons into account, the fuel consumption is added into consideration to make the model much more practical. An improved genetic algorithm is proposed for such a multi-UAVs multi-targets task allocation. Simulation results show that the algorithm is significantly effective and the allocation result is reasonable.
Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN...
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Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN) to improve the measurement accuracy of electronic compass. This method uses Fourier neural network to model electronic compass error, and adopts Adaptive Differential Evolution to optimize the weights of neural network, and get more exact error model to compensate measured values. The compensation object is the common electronic compass composed by two-dimensional magnetic resistance sensor. Compared with the compensation effect of Least-square method, BP neural network and Fourier neural networks, It proves that the mode of this method can realize the high precision in the sample space mapping and high non-linear approximation ability, and this method has faster convergence rate, can avoid falling into local minima, reduces the training error, and improves error compensation accuracy. This method decreases the error range from -3.4° ~ 25.2° before compensation to -0.20° ~ 0.72°, and the average of the absolute error is 0.30°. Repeatability tests also proved the compensation plan have a good consistency.
Abstract Common surveillance system is usually limited by the scope of observation, which may increase the probability of missing or misjudging suspicious targets. A wide-field monitoring system based on efficient ima...
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Abstract Common surveillance system is usually limited by the scope of observation, which may increase the probability of missing or misjudging suspicious targets. A wide-field monitoring system based on efficient image mosaicing algorithm is proposed in this paper. It can monitor a wide field for both short and long distance in real time and highlight the detected moving objects simultaneously. The image mosaicing is not only aimed at obtaining the wide field of view, but also utilized in motion detection for forming a wide-field motion mask. A simulation platform is developed to verify the performance of the system. The experiment results show that it satisfies the demands of high resolution and real-time implementation. The system is of high practical value to transportation surveillance or other applications.
Since the Pneumatic Muscle Actuator (PMA) has the characteristic of strong nonlinear and time lags, it is difficult to establish a precise mathematical mode. Model-Free Adaptive control (MFAC) is an advanced control a...
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ISBN:
(纸本)9781424490103
Since the Pneumatic Muscle Actuator (PMA) has the characteristic of strong nonlinear and time lags, it is difficult to establish a precise mathematical mode. Model-Free Adaptive control (MFAC) is an advanced control algorithm that does not require building an off-line mathematical model. This paper is basing on the feature of the PMA and presents a model-free adaptive control algorithm with the nonlinear feedback. Finally, experimental results show the strong robustness, fast response, and high precision of this control algorithm on the displacement control of the PMA.
Abstract Low-altitude penetration is one of the most important military applications of Unmanned Aerial Vehicles (UAVs). 3D route planning, which is a complex multi-objective optimization problem with multiple constra...
Abstract Low-altitude penetration is one of the most important military applications of Unmanned Aerial Vehicles (UAVs). 3D route planning, which is a complex multi-objective optimization problem with multiple constraints, is the keytechnology of UAV low altitude-penetration. A 3D route planning method based on Multi-Agent Genetic Algorithm (MAGA) is proposed in this paper. Specifically, a dynamic route representation form is proposed to improve the flight route accuracy. An efficient constraint handling method is used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation. More importantly, details are presented to show how to apply MAGA to 3D route planning while some necessary improvements of MAGA are proposed to make it more efficient. Simulation and analysis illustrate the effectiveness of this method.
Differential Evolution (DE) is a simple and efficient numerical optimization method. Most DE variants in the literature adopt fixed population size. This paper incorporates into DE the mechanisms of lifetime and extin...
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