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Boundary Gap Based Reactive Navigation in Unknown Environments

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 468-477页

作者： Zhao Gao Jiahu Qin Shuai Wang Yaonan Wang the Department of Automation University of Science and Technology of ChinaHefei 230027China the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082China

Due to the requirements for mobile robots to search or rescue in unknown environments,reactive navigation which plays an essential role in these applications has attracted increasing ***,most existing reactive methods are vulnerable to local minima in the absence of prior knowledge about the *** paper aims to address the local minimum problem by employing the proposed boundary gap(BG)based reactive navigation ***,the narrowest gap extraction algorithm(NGEA)is proposed to eliminate the improper ***,we present a new concept called boundary gap which enables the robot to follow the obstacle boundary and then get rid of local ***,in order to enhance the smoothness of generated trajectories,we take the robot dynamics into consideration by using the modified dynamic window approach(DWA).Simulation and experimental results show the superiority of our method in avoiding local minima and improving the smoothness.

关键词： Boundary gap local minima reactive navigation unknown environments

Brain-inspired chaotic spiking backpropagation

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National Science Review 2024年第6期11卷 169-179页

作者： Zijian Wang Peng Tao Luonan Chen Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory(Huangpu)

Spiking neural networks(SNNs) have superior energy efficiency due to their spiking signal transmission,which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable solution, trained SNNs frequently fall into local minima because they are still primarily based on gradient dynamics. Inspired by the chaotic dynamics in animal brain learning, we propose a chaotic spiking backpropagation(CSBP) method that introduces a loss function to generate brain-like chaotic dynamics and further takes advantage of the ergodic and pseudo-random nature to make SNN learning effective and robust. From a computational viewpoint, we found that CSBP significantly outperforms current state-of-the-art methods on both neuromorphic data sets(***-CIFAR10 and DVS-Gesture) and large-scale static data sets(e.g. CIFAR100 and Image Net) in terms of accuracy and robustness. From a theoretical viewpoint, we show that the learning process of CSBP is initially chaotic, then subject to various bifurcations and eventually converges to gradient dynamics,consistently with the observation of animal brain activity. Our work provides a superior core tool for direct SNN training and offers new insights into understanding the learning process of a biological brain.

关键词： spiking neural networks surrogate gradient local minima backpropagation brain-inspired learning chaos

Robust Iteration-dependent Least Mean Square-based Distribution Static Compensator Using Optimized PI Gains

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Chinese Journal of Electrical Engineering 2022年第4期8卷 79-90页

作者： Sabha Raj Arya Rakesh Maurya Jayadeep Srikakolapu Department of Electrical Engineering Sardar Vallabhbhai National Institute of TechnologySurat 395007India

A robust iteration-dependent least mean square(RIDLMS)algorithm-based fundamental extractor is developed to estimate the fundamental components of the load current for a four-wire DSTATCOM with a nonlinear *** averaging parameter for calculating the variable step size is iteration dependent and uses variable tuning *** than using the current value,the previous learning rate was used in this method to achieve a more adaptive *** additional control factor aids in determining the exact learning rate,resulting in reliable and convergent *** faster convergence rate and the avoidance of local minima make it *** estimation of the PI controller gains is achieved through a self-adaptive multi-population *** adaptive change in the group number will increase exploration and *** self-adaptive nature of the algorithm was used to determine the subpopulation number needed according to the fitness *** main advantage of this self-adaptive nature is the multi-population spread throughout the search space for a better optimal *** estimated gains of the PI controllers are used for the DC bus and AC terminal voltage error *** RIDLMS-based control with PI gains obtained using the proposed optimization algorithm showed better power quality *** considered RIDLMS-supported control was demonstrated experimentally using d-SPACE-1104.

关键词： Least mean square variable learning DSTATCOM local minima Rao algorithm reactive power neutral current

Search Space Pruning Based on Image Tools for Preliminary Interplanetary Trajectory Design

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Transactions of Nanjing University of Aeronautics and Astronautics 2015年第5期32卷 530-540页

作者：杨大林徐波高有涛 College of Astronautics Nanjing University of Aeronautics and Astronautics School of Astronomy & Space Science Nanjing University

A novel gravity assist space pruning(GASP)algorithm based on image tools is proposed for solving interplanetary trajectory optimization *** with traditional GASP algorithm,the concept of image is introduced to avoid missing interesting solutions with appropriate number of function *** tools allow us to evaluate the objective function in regions in place of points and provide an effective way to evaluate the forward and backward constraints for the multi-gravity assist trajectory optimization *** the interesting solutions of the interplanetary trajectory optimization problem are often clustered in a small portion of the search space rather than being overall evenly distributed,the regionwise evaluations with image tools make the little large interval with the proper Lipschitzian tolerances sampling *** detailed steps of the proposed method are presented and two examples including Earth Venus Mars(EVM)transfer and Earth Venus Venus Earth Jupiter Saturn(EVVEJS)transfer are ***,a comparison with solutions given by the literature demonstrates the effectiveness of the proposed method.

关键词： trajectory optimization global optimization local minima gravity assist space pruning (GASP) algorithm image tool

An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

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Computers, Materials & Continua 2021年第9期68卷 2961-2977页

作者： Mohamed Abdel-Basset Reda Mohamed Mohamed Abouhawwash Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Capability Systems Centre School of Engineering and ITUNSW CanberraAustralia Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the ***,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels ***,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal *** improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive ***-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed *** develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local *** two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR *** compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and *** was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with

关键词： Magnetic resonance imaging brain image segmentation artificial jellyfish search algorithm ranking method local minima Otsu method

Study on UAV Path Planning Approach Based on Fuzzy Virtual Force

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Chinese Journal of Aeronautics 2010年第3期23卷 341-350页

作者：董卓宁张汝麟陈宗基周锐北京航空航天大学自动化科学与电气工程学院西安飞行自动控制研究所

This article proposes a novel fuzzy virtual force （FVF） method for unmanned aerial vehicle （UAV） path planning in compli-cated environment. An integrated mathematical model of UAV path planning based on virtual force （VF） is constructed and the corresponding optimal solving method under the given indicators is presented. Specifically,a fixed step method is developed to reduce computational cost and the reachable condition of path planning is proved. The Bayesian belief network and fuzzy logic reasoning theories are applied to setting the path planning parameters adaptively,which can reflect the battlefield situation dy-namically and precisely. A new way of combining threats is proposed to solve the local minima problem completely. Simulation results prove the feasibility and usefulness of using FVF for UAV path planning. Performance comparisons between the FVF method and the A＊ search algorithm demonstrate that the proposed approach is fast enough to meet the real-time requirements of the online path planning problems.

关键词： fuzzy virtual force unmanned aerial vehicle path planning hybrid system Bayesian belief network fuzzy logic reasoning local minima

On the Solutions of One-Class-One-Outputed Feedforward Neural Network Classifiers Using Backpropagation Training

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On the Solutions of One-Class-One-Outputed Feedforward Neura...

The UAV cooperative formation control design with collision avoidance based on improved artificial potential field

1998 Fourth International Conference Signal Processing

作者： De-Shuang Huang Beijing Institute of System Engineering

＜正＞This paper studies the solutions of the outer-supervised batch-style learning based one-class-one-outputed（OCOO ） feedforward neural network（FNN） *** is proven that if the condition of the hidden neuron number being greater than the total training sample number, which is sufficient but not necessary,is satisfied, the network would uniquely converge to the global minima with null costs,and that the condition of the range space of the outer-supervised signal matrix being included in the range space of the hidden output matrix is sufficient and necessary condition of ensuring the local minima ***,in the case of the hidden neurons number being smaller than the total training samples number and greater than the output neurons number,it is shown that the global minimum solutions with null costs may also be derived by adequately adjusting the first layer *** the case of the hidden neurons number being smaller than the output neurons number,however, it is shown that there would not exist any global minima with null costs in the error surface.

关键词： Feedforward Neural Networks Error Cost Function Global minima local minima Outer-Supervised Learning

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The UAV cooperative formation control design with collision ...

Torque-Limited Attitude Control for Rigid Spacecraft with Motion Constraints

第30届中国控制与决策会议

作者： Jialong Zhang Jianguo Yan Dongli Yuan Xiaolei Hou Department of Automation Control Northwest Polytechnical University

Aiming at the problem that the collision avoidance process of the UVA is the existence of local minima by using the artificial potential field method, a compound vector artificial potential field method is proposed based on the virtual structure and the leader-follower control strategy in the three-dimensional space, which can avoid the obstacle effectively and then track the motion target. The attractive force of the artificial potential field can guide the virtual leader to track the target. At the same time, the follower tracks the leader to keep the formation flight. The effect of the repulsive force can avoid the collision between any two UAVs and arrange the followers evenly distributed on the spherical surface. Moreover, the follower’s specific order and position are not required. The collision path of the UAV formation depends on the artificial potential field with the two composite vectors, every UAV can choose the optimal path to avoid the obstacle and reconfigure the regular triangle formation flight after passing the obstacle. The simulation experiment verifies the validity and feasibility of the proposed the collision avoidance control algorithm and provides an idea for the collision avoidance of the Multi-UAV formation.

关键词： Artificial potential field method local minima Virtual leader Optimal path UAV formation avoidance obstacle

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Torque-Limited Attitude Control for Rigid Spacecraft with Mo...