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Online Path Planning for UAV Using an Improved Differential Evolution Algorithm

IFAC Proceedings Volumes 2011年第1期44卷 6349-6354页

作者： Xing Zhang Jie Chen Bin Xin Hao Fang School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China Tel: 86-10-68912464-22

Abstract This paper presents a 3D online path planning algorithm for unmanned aerial vehicle (UAV) flying in partially known hostile environment. In order to provide a smooth fight route for UAV, the algorithm adopts B-Spline curve to describe UAV's path whose control points are optimized by an improved differential evolution algorithm. The planner gradually produces a smooth path for UAV from starting location to its target. Regarding path planning performance, the proposed improved diferential evolution based planner is compared with its three competitors which are based on classical differential evolution, genetic algorithm and particle swarm optimization, respectively. Numerical simulation demonstrates that the proposed UAV path planner not only produces high-quality feasible flight routes for UAV, but also obviously outperforms its competitors.

关键词： UAV online path planning differential evolution B-Spline

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3D Multi-Constraint Route Planning for UAV Low-altitude Penetration Based on Multi-Agent Genetic Algorithm

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IFAC Proceedings Volumes 2011年第1期44卷 11821-11826页

作者： Jin-Ping Wu Zhi-Hong Peng Jie Chen School of Automation Beijing Institute of Technology Beijing 100081 Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Ministry of Education P. R. China

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 key technology 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.

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Decentralized Loading Coordinations for Large-population Plug-in Electric Vehicles and a few controllable Bulk Loads

Decentralized Loading Coordinations for Large-population Plu...

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IEEE Conference on Decision and control

作者： Xiaokun Yin Zhongjing Ma Lei Dong School of Automation Beijing Institute of Technology (BIT) Beijing China School of Automation BIT and the Key Laboratory of Complex System Intelligent Control and Decision (BIT) Ministry of Education Beijing China School of Automation BIT

ISBN: (纸本)9781612848006

This paper studies the loading coordinations for large-population autonomous individual (plug-in) electric vehicles (EVs) and a few controllable bulk loads, e.g. EV fleets, pumped storage hydro units, and so on. Due to the computational infeasibility of the centralized coordination methods to the underlying large-population systems, in this paper we develop a novel game-based decentralized coordination strategy. Following the proposed decentralized strategy update mechanism and under some mild conditions, the system may quickly converge to a nearly valley-fill Nash equilibrium. The results are illustrated with numerical examples.

关键词： (Plug-in) electric vehicles Loading coordination Nash equilibrium (NE) Valley-fill

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Mobile robot's electronic compass calibration based on modified Fourier Neural Network

Mobile robot's electronic compass calibration based on modif...

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IEEE Conference on Robotics, automation and Mechatronics

作者： Gong Kun Deng Fang Ma Tao Gong Kun is with School of Automation Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Deng Fang is with School of Automation Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Ma Tao is with School of Automation Beijing Institute of Technology and Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China

In order to improve the precision of the azimuth measured by mobile robot's electronic compass, this paper proposes a new calibration method based on Fourier Neural Network trained by Modified Particle Swarm Optimization (MPSO-FNN). This method makes use of Fourier Neural Network (FNN) to establish the error compensation model of electronic compass's azimuth, and introduces Modified Particle Swarm Optimization (MPSO) algorithm to optimize the weights of neural network. Thus the comparatively accurate error model of azimuth is obtained to compensate the output of electronic compass. This method not only has strong nonlinear approximation capability, but also overcomes the neural networks' shortcomings which are too slow convergence speed, oscillation, and easy to fall into local optimum and sensitive to the initial values. Experimental results demonstrate that after calibrated by this method, the range of azimuth error reduces to -0.35°~0.70° from -3.4°~25.2°, and the average value of absolute error is only 0.30°.

关键词： Compass Azimuth Biological neural networks Particle swarm optimization Training Accuracy automation

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Cooperative Pollution Supervising and Neutralization with Multi-actuator-sensor Network

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Acta Automatica Sinica 2011年第1期37卷 107-113页

作者： Wei SUN Li-Hua DOU Hao FANG School of Automation Beijing Institute of Technology Beijing 100081 P.R. China Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology) Ministry of Education Beijing 100081 P.R. China

The present work considers a scenario that a multi-actuator-sensor network neutralizes poisonous gas and tracks the pollution sources in a bounded area. A novel algorithm is proposed to minimize the system information uncertainty while reaching balance on the workload of actuators. The method combines the centroidal Voronoi tessellations (CVT) with a consensus strategy. The CVT of the region insures a local optimal position configuration of the actuators, thus the sensing uncertainty can be minimized. The consensus algorithm utilizes the connection information among actuators, and helps them to reach a common workload. The consensus component will be terminated or suppressed when the workload is averaged. The consensus component may postpone the realization of CVT configuration. But it could be viewed as a perturbation that helps the actuators jump out of the local optimal CVT configuration. As a result, the information uncertainty may be further reduced. Comparison is drawn between the pure CVT algorithm and the method with consensus strategy. Simulations validated the proposed approach.

关键词： Voronoi tessellation position configuration workload assignment multi-agent system

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Electronic compass calibration based on Adaptive Differential Evolution Algorithm-Fourier Neural Network

Electronic compass calibration based on Adaptive Differentia...

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作者： Gong, Kun Deng, Fang Chen, Jie School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China

ISBN: (纸本)9789881725592

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. © 2011 Chinese Assoc of Automati.

关键词： Error compensation

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Adaptive sliding mode control of electromechanical actuator with improved parameter estimation

Adaptive sliding mode control of electromechanical actuator ...

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Asian control Conference

作者： Hao Li Yongqiang Bai Zhong Su School of Automation Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

An adaptive sliding mode control scheme for electromechanical actuator has been presented. The adaptive control strategy can estimate the uncertain parameters and adaptively compensate the modeled dynamical uncertainties, while the sliding mode control method overcomes the unmodelled dynamics. In the adaptive law an equivalent output injection of the sliding mode observer which contains the parameter estimation error is used, and estimates of parameters can approximate the true values without prediction-error that is typically used in compositive adaptive law. Due to the improved estimation of uncertain parameters, the sliding mode law can robustifies the design against model uncertainties with a small swithcing gain. Stability of the system with the proposed approach has been proved and it has also been shown that the system states can reach the sliding mode in finite time. Finally, the effectiveness of the proposed control scheme has been exhibited via simulation examples.

关键词： Parameter estimation Sliding mode control Actuators Aerodynamics Robustness Adaptive control Uncertainty

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Path planning of multiple UAVs low-altitude penetration based on improved Multi-agent Coevolutionary Algorithm

Path planning of multiple UAVs low-altitude penetration base...

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作者： Peng, Zhi-Hong Sun, Lin Chen, Jie Wu, Jin-Ping School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9789881725592

In order to solve the multi-UAV cooperative path planning problem of low-altitude penetration, the paper proposes an improved Multi-agent Coevolutionary Algorithm (IMACEA), which introduces co-evolution mechanism based on Multi-agent Genetic Algorithm(MAGA) to find the optimal solution of multi-objective optimization problem by combing the agents' perception and response capabilities of environment, information sharing capacity among multi-agent systems and heuristic search ability of heuristic search. At the same time, we use absolute Cartesian coordinates and relative polar coordinates coding method to reduce the search space and speed up the convergence rate. To adapt to multi-path planning problems of UAVs, new multi-agent co-evolution operators are designed. Finally, the proposed algorithm is used for multiple UAVs offline and online route planning, simulation results show the effectiveness of the algorithm. © 2011 Chinese Assoc of Automati.

关键词： Motion planning

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Differential evolution with adaptive population size combining lifetime and extinction mechanisms

Differential evolution with adaptive population size combini...

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Asian control Conference

作者： Chunmei Zhang Jie Chen Bin Xin Tao Cai Chen Chen School of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China School of Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9781612844879

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 extinction which regulate DE's population size in an adaptive manner. The population size is adjusted according to the online progress of fitness improvement. Two schemes of inserting new individuals are proposed to match different mechanisms respectively. The performance of these innovations is examined through the optimization of benchmark problems. The results show that the proposed adaptive population sizing strategy is efficient for improving the convergence and efficiency of the DE.

关键词： Convergence Optimization Benchmark testing Cloning automation Evolutionary computation Algorithm design and analysis

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Video vehicle detection through multiple background-based features and statistical learning

Video vehicle detection through multiple background-based fe...

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International Conference on intelligent Transportation

作者： Mingxia Sun Kunfeng Wang Ming Tang Fei-Yue Wang Jinfeng Yang Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences (CAS) Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences (CAS) Beijing China

This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.

关键词： Vehicles Feature extraction Training Image edge detection Vehicle detection Meteorology Radio frequency

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