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Decentralized Multirobotic Fish pursuit Control With Attraction-Enhanced Reinforcement Learning

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Feng, Yukai Wu, Zhengxing Wang, Jian Gu, Junwen Yu, Fuyang Yu, Junzhi Tan, Min Chinese Acad Sci Inst Automat Key Lab Cognit & Decis Intelligence Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Adaptive and efficient cooperative control is a crucial capability for multirobotic fish systems, as it can substantially enhance their performance in complex underwater tasks. The pursuit and evasion dynamics in such topics have gained significant attention from the scientific community. In this article, we present a novel adaptive algorithm tailored specifically for cooperative pursuit among multirobotic fish systems. Benefiting from the integration of attraction mechanisms and reinforcement learning techniques, the proposed method empowers the robotic fish to make adaptive decisions based on local observations and environmental cues. Meanwhile, a state transition environment has been customized to the unique dynamics of robotic fish, equipping the cooperative pursuit strategy to fulfill practical application requirements and facilitate adaptation across diverse platforms. Besides, based on the curriculum learning approach, a decentralized pursuit policy is also formulated and implemented within the developed robotic fish system. Simulations and real-world experiments have validated the efficiency and adaptability of this cooperative pursuit strategy. This research offers valuable insights and contributions to the exploration of cooperative control in multirobotic fish systems, addressing the critical challenge of achieving adaptive and efficient coordination in complex underwater environments.

关键词： Robots Robot kinematics Reinforcement learning Sharks Force Training Biomimetics Vehicle dynamics Service robots pursuit algorithms Cooperating robots deep reinforcement learning multirobotic systems robotic fish

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Robot Differential Drive Navigation Through Probabilistic Roadmap and Pure pursuit

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IEEE ACCESS 2025年 13卷 22118-22132页

作者： Ali, Maram Das, Saptarshi Townley, Stuart Univ Exeter Fac Environm Sci & Econ Ctr Environm Math Penryn Campus Penryn TR10 9FE Cornwall England King Khalid Univ Coll Sci Dept Math Abha 62223 Saudi Arabia Univ Exeter Inst Data Sci & Artificial Intelligence Exeter EX4 4QE Devon England Univ Exeter Environm & Sustainabil Inst Penryn Campus Penryn TR10 9FE Cornwall England

This research addresses autonomous navigation for differential drive robots by integrating probabilistic roadmap (PRM) and pure pursuit algorithms. The proposed method innovatively tackles path planning challenges in complex environments by developing a novel framework that combines forward kinematics, binary occupancy mapping, and adaptive path generation. The approach demonstrates superior navigation performance through efficient path planning in constrained spaces, validated by a case study in Riyadh, Saudi Arabia. By using PRM's probabilistic path generation and pure pursuit's real-time control, the method outperforms traditional navigation techniques in generating feasible trajectories through intricate environments. Key contributions include a comprehensive framework for robot navigation that offers enhanced adaptability and robust path planning. While being limited to static environments, the research provides a foundational approach for developing more resilient autonomous robotic systems, setting the stage for future navigation algorithm advancements.

关键词： Robots Navigation Path planning Planning Real-time systems Kinematics Mobile robots Heuristic algorithms pursuit algorithms Wheels Differential drive navigation probabilistic roadmap pure pursuit path planning unexplored terrains

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Development and Implementation of Safe, Human-like Autonomous Vehicle Control on Horizontal Curves

Development and Implementation of Safe, Human-like Autonomou...

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International Conference on Mechatronics (ICM)

作者： Michael Azage Jose Matute Ali Karimoddini Department of Electrical and Computer Engineering North Carolina Agricultural and Technical State University Greensboro North Carolina USA Virginia Tech Transportation Institute Blacksburg VA USA

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

Recent advancements in deep learning and hard-ware technologies have enabled the application of novel control techniques for autonomous vehicles to handle challenging road conditions such as horizontal curves. This study presents a novel approach that blends human driver data with the Pure pursuit algorithm, utilizing the law of propagation of uncertainty. Trained on the combined data, two deep learning architectures - Long Short-Term Memory (LSTM) networks and Fully Connected Deep Neural Networks (FCDNN) - are adopted: one predicts future speed profiles for calculating braking deceleration. In contrast, the other predicts steering rates for tracking a reference path, considering lateral acceleration and jerk constraints to ensure safety and comfort following the American Association of State Highway and Transportation Officials (AASHTO) and International Organization for Standardization (ISO) 15622 standards while emulating an expert human driving behavior. The proposed work has been implemented and tested using a Chevy Bolt Electric Utility Vehicle (EUV) during a U-turn, with test results demonstrating successful constraint satisfaction and acceptable cross-track error in reference path tracking.

关键词： Uncertainty Mechatronics Roads Power industry Safety Autonomous vehicles Long short term memory Standards pursuit algorithms Vehicles

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Block Compressive Sensing Based Multi-User Detection for Uplink Grant-Free Codebook-Based NOMA

Block Compressive Sensing Based Multi-User Detection for Upl...

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National Conference on Communications (NCC)

作者： Vinjamoori Vikas A. Rajesh Kuntal Deka Sanjeev Sharma Indian Institute of Technology Guwahati India Indian Institute of Technology (BHU) Varanasi

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

A codebook (CB)-based non-orthogonal multiple access (NOMA) is an efficient variant of code-domain (CD) NOMA (CD-NOMA) that leverages multi-dimensional constellations (MDC) to achieve significant performance gains over spread-sequence CD-NOMA. This paper focuses on developing block-sparsity models (Model-1 and Model-2) for grant-free CB-based NOMA systems. The frame-wise static block sparsity model exhibits both block and structured sparsity, enhancing detection performance. Furthermore, this work includes the design of a multi-user detection algorithm for joint activity and data detection (JADD) tailored to the aforementioned models. The inherent block-sparsity structure in grant-free CB-based NOMA systems is leveraged to develop a block-compressive sensing (BCS) framework for multi-user detection. This framework achieves robust reconstruction performance with minimal computational complexity. The paper introduces a first significant jump (FSJ)-aided block subspace pursuit algorithm (FSJ-BSP), which performs JADD without requiring prior knowledge of the sparsity level. This adaptability makes the proposed algorithm well-suited for practical 6G networks.

关键词： Multiuser detection NOMA Computational modeling Performance gain Data models Sensors Uplink Computational complexity pursuit algorithms Compressed sensing

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Robust independent component analysis algorithms for projection pursuit

Robust independent component analysis algorithms for project...

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IEEE International Conference on Systems, Man and Cybernetics

作者： R. Thawonmas J. Cao Department of Information Systems Engineering Kochi University of Technology Kochi Japan Department of Electrical and Electronics Engineering Sophia University Tokyo Japan

This paper presents the derivation of batch-mode neural algorithms which seek to robustly identify interesting projections of high dimensional data. The new index for projection pursuit, is a measure of the difference between a platykurtic density and a leptokurtic density. The robustness experiment is conducted to verify the validity of the proposed index when the algorithms are applied to artificial data and commonly used benchmark "crab" data.

关键词： Robustness Independent component analysis pursuit algorithms Signal processing algorithms Biomedical measurements Density measurement Neural networks Data mining Linear discriminant analysis Information systems

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An algorithm of matched pursuit with compensation for the analysis of multicomponent Doppler signals

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JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 2016年第10期61卷 1107-1114页

作者： Fedosov, V. P. Evdokimova, E. O. Southern Fed Univ Nekrasovskii Per 44 Taganrog 347922 Russia

The problem of decomposition of multicomponent Doppler signals in order to increase the operation efficiency of systems based on the use of Doppler radars for a wide range of applications is considered. To study the structure of these signals, a matched pursuit algorithm and its modification-matched pursuit with compensation, which have been developed for the analysis of signals represented by functions of time and their spectrograms, respectively, are used. Obtained and analyzed the Results of decomposition of a simulated three-component signal against the background of noise for a signal-to-noise ratio of 20 dB as well as actual Doppler signals by these algorithmsare obtained and analyzed.

关键词： Signal analysis operating efficiency pursuit algorithms Doppler effect BACKGROUND NOISE signals compensated Doppler radar

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Batch algorithms of matching pursuit and orthogonal matching pursuit with applications to compressed sensing

Batch algorithms of matching pursuit and orthogonal matching...

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International Conference on Information and Automation (ICIA)

作者： Huiyuan Wang Jose Vieira Paulo Ferreira Bruno Jesus Isabel Duarte IEETA Universidade de Aveiro Aveiro Portugal School of Information Science and Engineering Shandong University Jinan Shandong China

Batch algorithms of matching pursuit (MP) and orthogonal matching pursuit (OMP) are proposed in this paper. In both algorithms, the original iteration procedures are modified in the following way. Instead of finding a single best-matched atom in each iteration, we find a number of best-matched atoms to speed up the convergence, - a batch version. Then optimized coefficients are computed based on these atoms. Numerical simulations in the application to compressed sensing show that the proposed algorithms are much faster than the original ones, while similar reconstruction precision is obtained.

关键词： Matching pursuit algorithms pursuit algorithms Compressed sensing Image reconstruction Numerical simulation Image coding Image sampling Greedy algorithms Linear systems Automation

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Finite time analysis of the pursuit algorithm for learning automata

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1996年第4期26卷 590-598页

作者： Rajaraman, K Sastry, PS Department of Electrical Engineering Indian Institute of Science Bangalore India

The problem of analyzing the finite time behavior of learning automata is considered, This problem involves the finite time analysis of the learning algorithm used by the learning automaton and is important in determining the rate of convergence of the automaton, In this paper, a general framework for analyzing the finite time behavior of the automaton learning algorithms is proposed, Using this framework, the finite time analysis of the pursuit Algorithm is presented, We have considered both continuous and discretized forms of the pursuit algorithm, Based on the results of the analysis, we compare the rates of convergence of these two versions of the pursuit algorithm, At the end of the paper, we also compare our framework with that of Probably Approximately Correct (PAC) learning.

关键词： Algorithm design and analysis pursuit algorithms Learning automata Convergence

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A Conclusive Analysis of the Finite-Time Behavior of the Discretized pursuit Learning Automaton

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第1期31卷 284-294页

作者： Zhang, Xuan Jiao, Lei Oommen, B. John Granmo, Ole-Christoffer Univ Agder Ctr Artificial Intelligence Res N-4879 Grimstad Norway Confirmit AS N-4878 Grimstad Norway Univ Agder Dept ICT N-4879 Grimstad Norway Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada

This paper deals with the finite-time analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steady-state analysis for which there are, probably, scores of papers. As clarified later, unarguably, the FTA of Markov chains, in general, and of LA, in particular, is far more complex than the asymptotic steady-state analysis. Such an FTA provides rigid bounds for the time required for the LA to attain to a given convergence accuracy. We concentrate on the FTA of the Discretized pursuit Automaton (DPA), which is probably one of the fastest and most accurate reported LA. Although such an analysis was carried out many years ago, we record that the previous work is flawed. More specifically, in all brevity, the flaw lies in the wrongly "derived" monotonic behavior of the LA after a certain number of iterations. Rather, we claim that the property should be invoked is the submartingale property. This renders the proof to be much more involved and deep. In this paper, we rectify the flaw and reestablish the FTA based on such a submartingale phenomenon. More importantly, from the derived analysis, we are able to discover and clarify, for the first time, the underlying dilemma between the DPA's exploitation and exploration properties. We also nontrivially confirm the existence of the optimal learning rate, which yields a better comprehension of the DPA itself.

关键词： Convergence Markov processes Learning automata Eigenvalues and eigenfunctions pursuit algorithms Maximum likelihood estimation Discretized pursuit automaton (DPA) finite-time analysis (FTA) learning automaton pursuit algorithms (PAs)

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Polar n-Complex and n-Bicomplex Singular Value Decomposition and Principal Component pursuit

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2016年第24期64卷 6533-6544页

作者： Chan, Tak-Shing T. Yang, Yi-Hsuan Acad Sinica Res Ctr Informat Technol Innovat Taipei 11564 Taiwan

Informed by recent work on tensor singular value decomposition and circulant algebra matrices, this paper presents a new theoretical bridge that unifies the hypercomplex and tensor-based approaches to singular value decomposition and robust principal component analysis. We begin our work by extending the principal component pursuit to Olariu's polar n-complex numbers as well as their bicomplex counterparts. In doing so, we have derived the polar n-complex and n-bicomplex proximity operators for both the l(1)- and trace-norm regularizers, which can be used by proximal optimization methods such as the alternating direction method of multipliers. Experimental results on two sets of audio data show that our algebraically informed formulation outperforms tensor robust principal component analysis. We conclude with the message that an informed definition of the trace norm can bridge the gap between the hypercomplex and tensor-based approaches. Our approach can be seen as a general methodology for generating other principal component pursuit algorithms with proper algebraic structures.

关键词： Hypercomplex tensors singular value decomposition principal component pursuit algorithms

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