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International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Hao Li Jie Luo Shu Yan Min Zhu Qianyang Hu Zhennan Liu School of Automation Wuhan University of Technology Wuhan China School of Automotive Engineering Wuhan University of Technology Wuhan China Zhengzhou yutong bus Co. Ltd. Henan China

Compared with small vehicles, the bus body is longer and the parking path is relatively shorter. If there are some errors in the path tracking process, there will be the risk of crossing the parking line. Therefore, the parking system of bus needs a high precision path tracking algorithm. Pure pursuit algorithm is widely used in vehicle or robot path tracking problem, but its performance depends on the selection of look-ahead distance which needs to be determined by scene in practical application. Aiming at the performance requirement of bus parking system, this paper improves the pure pursuit algorithm by analyzing the stability and error sources of the pure pursuit algorithm. In the improvement of the algorithm, speed is used as one of the parameters in the calculation of look-ahead distance to improve the stability of the system, and PI (Proportion Integration) control is added to the calculation of steering angle of pure pursuit algorithm to improve the tracking accuracy of the algorithm in the curve stage. The results of experiments show that the proposed algorithm can effectively improve the parking accuracy and stability of buses.

关键词： pursuit algorithms Trajectory Stability analysis Wheels Kinematics Target tracking Axles

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A formal proof of the ε-optimality of discretized pursuit algorithms

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APPLIED INTELLIGENCE 2016年第2期44卷 282-294页

作者： Zhang, Xuan Oommen, B. John Granmo, Ole-Christoffer Jiao, Lei Univ Agder Dept ICT Grimstad Norway Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada

Learning Automata (LA) can be reckoned to be the founding algorithms on which the field of Reinforcement Learning has been built. Among the families of LA, Estimator algorithms (EAs) are certainly the fastest, and of these, the family of discretized algorithms are proven to converge even faster than their continuous counterparts. However, it has recently been reported that the previous proofs for oee--optimality for all the reported algorithms for the past three decades have been flawed. We applaud the researchers who discovered this flaw, and who further proceeded to rectify the proof for the Continuous pursuit Algorithm (CPA). The latter proof examines the monotonicity property of the probability of selecting the optimal action, and requires the learning parameter to be continuously changing. In this paper, we provide a new method to prove the oee--optimality of the Discretized pursuit Algorithm (DPA) which does not require this constraint, by virtue of the fact that the DPA has, in and of itself, absorbing barriers to which the LA can jump in a discretized manner. Unlike the proof given (Zhang et al., Appl Intell 41:974-985, 3) for an absorbing version of the CPA, which utilizes the single-action Hoeffding's inequality, the current proof invokes what we shall refer to as the "multi-action" version of the Hoeffding's inequality. We believe that our proof is both unique and pioneering. It can also form the basis for formally showing the oee--optimality of the other EAs that possess absorbing states.

关键词： Machine learning Learning automata pursuit algorithms DPA Convergence epsilon-optimality

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Ghosting suppression for incremental principal component pursuit algorithms

Ghosting suppression for incremental principal component pur...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Paul Rodríguez Brendt Wohlberg Department of Electrical Engineering Pontificia Universidad Católica del Perú Lima Peru T-5 Applied Mathematics and Plasma Physics Los Alamos National Laboratory Los Alamos NM USA

ISBN: (纸本)9781509045464

In video background modeling, ghosting occurs when an object that belongs to the background is assigned to the foreground. In the context of Principal Component pursuit, this usually occurs when a moving object occludes a high contrast background object, a moving object suddenly stops, or a stationary object suddenly starts moving. Based on a previously developed incremental PCP method, we propose a novel algorithm that uses two simultaneous background estimates based on observations over the previous n 1 and n 2 (n 1 C n 2 ) frames in order to identify and diminish the ghosting effect. Our computational results show that the proposed method greatly improves both the subjective quality and accuracy as determined by the F-measure.

关键词： Context Adaptation models Estimation Computational modeling Robustness pursuit algorithms Electrical engineering

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Decentralized Multirobotic Fish pursuit Control With Attraction-Enhanced Reinforcement Learning

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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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The Hierarchical Discrete pursuit Learning Automaton: A Novel Scheme With Fast Convergence and Epsilon-Optimality

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第6期35卷 8278-8292页

作者： Omslandseter, Rebekka Olsson Jiao, Lei Zhang, Xuan Yazidi, Anis Oommen, B. John Univ Agder Dept Informat & Commun Technol N-4879 Grimstad Norway Norwegian Res Ctr NORCE N-4879 Grimstad Norway Oslo Metropolitan Univ Dept Comp Sci N-0160 Oslo Norway Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada Northwest Univ TRADE Res Entity ZA-2520 Potchefstroom South Africa

Since the early 1960s, the paradigm of learning automata (LA) has experienced abundant interest. Arguably, it has also served as the foundation for the phenomenon and field of reinforcement learning (RL). Over the decades, new concepts and fundamental principles have been introduced to increase the LA's speed and accuracy. These include using probability updating functions, discretizing the probability space, and using the "pursuit" concept. Very recently, the concept of incorporating "structure" into the ordering of the LA's actions has improved both the speed and accuracy of the corresponding hierarchical machines, when the number of actions is large. This has led to the epsilon-optimal hierarchical continuous pursuit LA (HCPA). This article pioneers the inclusion of all the above-mentioned phenomena into a new single LA, leading to the novel hierarchical discretized pursuit LA (HDPA). Indeed, although the previously proposed HCPA is powerful, its speed has an impediment when any action probability is close to unity, because the updates of the components of the probability vector are correspondingly smaller when any action probability becomes closer to unity. We propose here, the novel HDPA, where we infuse the phenomenon of discretization into the action probability vector's updating functionality, and which is invoked recursively at every stage of the machine's hierarchical structure. This discretized functionality does not possess the same impediment, because discretization prohibits it. We demonstrate the HDPA's robustness and validity by formally proving the epsilon-optimality by utilizing the moderation property. We also invoke the submartingale characteristic at every level, to prove that the action probability of the optimal action converges to unity as time goes to infinity. Apart from the new machine being epsilon-optimal, the numerical results demonstrate that the number of iterations required for convergence is significantly reduced for the HDPA, when

关键词： Learning automata Automata Convergence Reinforcement learning Task analysis pursuit algorithms Markov processes Convergence analysis hierarchical discrete pursuit LA learning automata (LA) reinforcement learning (RL)

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A formal proof of the ε-optimality of absorbing continuous pursuit algorithms using the theory of regular functions

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APPLIED INTELLIGENCE 2014年第3期41卷 974-985页

作者： Zhang, Xuan Granmo, Ole-Christoffer Oommen, B. John Jiao, Lei Univ Agder Dept ICT Grimstad Norway Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada

The most difficult part in the design and analysis of Learning Automata (LA) consists of the formal proofs of their convergence accuracies. The mathematical techniques used for the different families (Fixed Structure, Variable Structure, Discretized etc.) are quite distinct. Among the families of LA, Estimator algorithms (EAs) are certainly the fastest, and within this family, the set of pursuit algorithms have been considered to be the pioneering schemes. Informally, if the environment is stationary, their epsilon-optimality is defined as their ability to converge to the optimal action with an arbitrarily large probability, if the learning parameter is sufficiently small/large. The existing proofs of all the reported EAs follow the same fundamental principles, and to clarify this, in the interest of simplicity, we shall concentrate on the family of pursuit algorithms. Recently, it has been reported Ryan and Omkar (J Appl Probab 49(3):795-805, 2012) that the previous proofs for epsilon-optimality of all the reported EAs have a common flaw. The flaw lies in the condition which apparently supports the so-called "monotonicity" property of the probability of selecting the optimal action, which states that after some time instant t (0), the reward probability estimates will be ordered correctly forever. The authors of the various proofs have rather offered a proof for the fact that the reward probability estimates are ordered correctly at a single point of time after t (0), which, in turn, does not guarantee the ordering forever, rendering the previous proofs incorrect. While in Ryan and Omkar (J Appl Probab 49(3):795-805, 2012), a rectified proof was presented to prove the epsilon-optimality of the Continuous pursuit Algorithm (CPA), which was the pioneering EA, in this paper, a new proof is provided for the Absorbing CPA (ACPA), i.e., an algorithm which follows the CPA paradigm but which artificially has absorbing states whenever any action probability is arbitrarily c

关键词： pursuit algorithms CPA Absorbing CPA epsilon-optimality

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Real-Time Data Sensing for Microseismic Monitoring via Adaptive Compressed Sampling

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IEEE SENSORS JOURNAL 2023年第10期23卷 10644-10655页

作者： Chen, Liang Lan, Zhiqiang Qian, Shuo Hou, Xiaojuan Zhang, Le He, Jian Chou, Xiujian North Univ China State Sci & Technol Elect Test & Measurement Lab Taiyuan 030051 Shanxi Peoples R China Soochow Univ Sch Future Sci & Engn Suzhou 215299 Peoples R China North Univ China Sch Software Taiyuan 030051 Shanxi Peoples R China

The large amount of monitoring data has posed enormous challenges to the quick response and accurate analysis of microseismic events. Compressed sensing (CS) has the advantages of low resource cost, high efficiency, and excellent data compression ratio (CR), over conventional sensing methods. However, there are still issues to be addressed for its applications: 1) the poor quality and complex signal structure significantly increased the difficulty of keeping satisfactory efficiency;2) the systematic design of the sparse dictionary, and the measurement matrix for microseismic signal CS are still poor;and 3) the conventional recovery algorithms also require prior knowledge of signal sparsity, which is hardly possible to know or estimate in practice. Therefore, an adaptive real-time sensing method for microseismic monitoring from the perspective of systematic design was proposed in this work. We first analyzed noise and signal structure characteristics to construct an over-complete learning dictionary. Second, according to the learned dictionary, we analyzed the key performance factors of random projection through comparison between different matrices. Third, we explored the relationship between the signal sparsity and the residual energy decay during data recovery with the greedy pursuit algorithms and then presented an energy-ratio-based sparsity adaptive matching algorithm. Finally, we carried out the performance evaluation of the proposed real-time sensing method through synthetic signals and field monitoring data.

关键词： Sensors Dictionaries Sparse matrices Monitoring pursuit algorithms Noise measurement Matching pursuit algorithms Compressed sensing (CS) microseismic monitoring real-time sensing sparsity adaptive

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Randomized Simultaneous Hard Thresholding pursuit Algorithm for Multiple Measurement Vectors 14

Randomized Simultaneous Hard Thresholding Pursuit Algorithm ...

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14th IEEE International Conference on Signal Processing and Communications (SPCOM)

作者： Bapat, Ketan Atul Chakraborty, Mrityunjoy Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur W Bengal India

ISBN: (数字)9781665482509

ISBN: (纸本)9781665482509

In this paper, we propose a new algorithm named Randomized Simultaneous Hard Thresholding pursuit(RSHTP) for the multiple measurements vector (MMV) problem in compressed sensing. In the proposed algorithm, the gradient is calculated only with respect to few of the signals at each iteration that are chosen randomly. This reduces the computational cost which is significant when the problem size is large. A deterministic convergence analysis is carried out where we present theoretical guarantees using the restricted isometric property (RIP). Simulation studies show that the proposed algorithm enjoys at par performance even at a moderate rate of column selection in each iteration.

关键词： Conferences Computational modeling Signal processing algorithms Signal processing Computational efficiency pursuit algorithms Compressed sensing

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Decentralized Multi-Robot pursuit of an Evader in Obstacle Environments

Decentralized Multi-Robot Pursuit of an Evader in Obstacle E...

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Asian Control Conference

作者： Kai Rao Huaicheng Yan Penghui Yang Yunkai Lv Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, we introduce a decentralized algorithm for pursuing an evader with identical motion capabilities in environments with obstacles. Initially, we propose the Safe pursuit Buffered Voronoi Cell (SPBVC), which ensures the safety of robots during the pursuit, encompassing collision avoidance with obstacles, reciprocal avoidance among pursuing robots, and avoidance between the pursuers and the evader. Subsequently, based on SPBVC, distinct pursuit algorithms suitable for various robots are developed, where each pursuer continually computes the SPBVC and plans the control actions until the evader is incapacitated. Furthermore, we provide a proof of the safety assurance offered by the SPBVC during the pursuit. Ultimately, we validate the overall algorithm through simulations in both two-dimensional and three-dimensional scenarios.

关键词： Solid modeling Computational modeling Process control Safety Collision avoidance Robots pursuit algorithms

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