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Efficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM

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Scientific reports 2025年第1期15卷 18331页

作者： Yunhan Lin Zhijie Zhang Yijian Tan Hao Fu Huasong Min School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 China. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430081 China. Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan 430081 China. Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan 430081 China. mhuasong@***.

To address the challenges of sample utilization efficiency and managing temporal dependencies, this paper proposes an efficient path planning method for mobile robot in dynamic environments based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm. The proposed method, named PL-TD3, integrates prioritized experience replay (PER) and long short-term memory (LSTM) neural networks, which enhance both sample efficiency and the ability to handle time-series data. To verify the effectiveness of the proposed method, simulation and practical experiments were designed and conducted. In the simulation experiments, both static and dynamic obstacles were included in the test environment, along with experiments to assess generalization capabilities. The algorithm demonstrated superior performance in terms of both execution time and path efficiency. The practical experiments, based on the assumptions from the simulation tests, further confirmed that PL-TD3 has improved the effectiveness and robustness of path planning for mobile robot in dynamic environments.

关键词： Long short-term memory (LSTM ) Path planning in dynamic environment Prioritized experience replay (PER) Reinforcement learning Twin delayed deep deterministic policy gradient (TD3) algorithm

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Residual Squeeze-and-Excitation Network with Multi-scale Spatial Pyramid Module for Fast Robotic Grasping Detection

Residual Squeeze-and-Excitation Network with Multi-scale Spa...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hu Cao Guang Chen Zhijun Li Jianjie Lin Alois Knoll Chair of Robotics Artificial Intelligence and Real-Time Systems Technische Universität München München Germany State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Tongji University Shanghai China University of Science and Technology of China China

This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.

关键词： Automation Conferences Pose estimation Grasping Manipulators Feature extraction Inference algorithms

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification

Empowering Leather Quality Assurance: Leveraging Convolution...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Logeswaran K Suresh P Savitha S Swati Sah Prasanna Kumar Kr Rajadevi R Vasugi M Sujit B Akilesh T Department of Artificial Intelligence Center of Excellence in Real-Time Analytic Kongu Engineering College Erode Tamilnaud India Department of Database Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode Tamil Nadu India School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Information Technology Kongu Engineering College Erode Tamilnaud India Department of Artificial Intelligence Kongu Engineering College Erode Tamilnaud India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed.

关键词： Deep learning Visualization Quality assurance Accuracy Quality control Product design Quality assessment Convolutional neural networks Reliability Defect detection

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Multi-agent systems with memories under DoS attacks

Multi-agent systems with memories under DoS attacks

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International Conference on Control, Automation, robotics and Vision (ICARCV)

作者： Ricardo Almeida Ewa Girejko Luís Machado Agnieszka B. Malinowska Natália Martins Department of Mathematics Center for Research and Development in Mathematics and Applications University of Aveiro Portugal Faculty of Computer Science Bialystok University of Technology Białystok Poland Institute of Systems and Robotics - University of Coimbra Coimbra Portugal Department of Mathematics University of Trás-os-Montes e Alto Douro (UTAD) Vila Real Portugal

ISBN: (纸本)9781665476881

The paper is devoted to multi-agent systems with memories under denial-of-service (DoS) attacks. The memory is represented by the fractional derivative that appears in the dynamics of the considered systems. The problem that we aim to address is, under which conditions the system continues to be stable or is in consensus mode, despite DoS attacks. Solutions to these problems are proposed in the presented theorems. We strengthen the theoretical considerations with numerical simulations.

关键词： Linear systems Sufficient conditions Automation Denial-of-service attack Numerical simulation Stability analysis Robots

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Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation

arXiv

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arXiv 2020年

作者： Esterle, Klemens Kessler, Tobias Knoll, Alois fortiss GmbH Research Institute of the Free State of Bavaria Munich Germany Chair of Robotics Artificial Intelligence and Real-time Systems Technical University of Munich Munich Germany

Mixed-Integer Quadratic Programming (MIQP) has been identified as a suitable approach for finding an optimal solution to the behavior planning problem with low runtimes. Logical constraints and continuous equations are optimized alongside. However, it has only been formulated for a straight road, omitting common situations such as taking turns at intersections. This has prevented the model from being used in reality so far. Based on a triple integrator model formulation, we compute the orientation of the vehicle and model it in a disjunctive manner. That allows us to formulate linear constraints to account for the non-holonomy and collision avoidance. These constraints are approximations, for which we introduce the theory. We show the applicability in two benchmark scenarios and prove the feasibility by solving the same models using nonlinear optimization. This new model will allow researchers to leverage the benefits of MIQP, such as logical constraints, or global optimality. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation

Optimal Behavior Planning for Autonomous Driving: A Generic ...

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IEEE Symposium on Intelligent Vehicle

作者： Klemens Esterle Tobias Kessler Alois Knoll Fortiss GmbH Research Institute of the Free State of Bavaria Munich Germany Chair of Robotics Artificial Intelligence and Real-time Systems Technical University of Munich Munich Germany

ISBN: (数字)9781728166735

ISBN: (纸本)9781728166742

关键词： Acceleration Mathematical model Planning Trajectory Roads Computational modeling Vehicle safety

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Modeling and testing multi-agent traffic rules within interactive behavior planning

arXiv

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arXiv 2020年

作者： Esterle, Klemens Gressenbuch, Luis Knoll, Alois Rortiss GmbH Research Institute of the Free State of Bavaria Munich Germany Robotics Artificial Intelligence and Real-time Systems Technische Universität München Munich Germany

Autonomous vehicles need to abide by the same rules that humans follow. Some of these traffic rules may depend on multiple agents or time. Especially in situations with traffic participants that interact densely, the interactions with other agents need to be accounted for during planning. To study how multi-agent and time-dependent traffic rules shall be modeled, a framework is needed that restricts the behavior to ruleconformant actions during planning, and that can eventually evaluate the satisfaction of these rules. This work presents a method to model the conformance to traffic rules for interactive behavior planning and to test the ramifications of the traffic rule formulations on metrics such as collision, progress, or rule violations. The interactive behavior planning problem is formulated as a dynamic game and solved using Monte Carlo Tree Search, for which we contribute a new method to integrate history-dependent traffic rules into a decision tree. To study the effect of the rules, we treat it as a multi-objective problem and apply a relaxed lexicographical ordering to the vectorized rewards. We demonstrate our approach in a merging scenario. We evaluate the effect of modeling and combining traffic rules to the eventual compliance in simulation. We show that with our approach, interactive behavior planning while satisfying even complex traffic rules can be achieved. Moving forward, this gives us a generic framework to formalize traffic rules for autonomous vehicles. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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Formalizing Traffic Rules for Machine Interpretability

Formalizing Traffic Rules for Machine Interpretability

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Connected and Automated Vehicles Symposium (CAVS)

作者： Klemens Esterle Luis Gressenbuch Alois Knoll fortiss GmbH Research Institute of the Free State of Bavaria Munich Germany Robotics Artificial Intelligence and Real-time Systems Technische Universität München Munich Germany

ISBN: (数字)9781728190013

ISBN: (纸本)9781728190020

Autonomous vehicles need to be designed to abide by the same rules that humans follow. This is challenging, because traffic rules are fuzzy and not well defined, making them incomprehensible to machines. Satisfaction cannot be incorporated in a planning component without proper formalization, nor can it be monitored and verified during simulation or testing. However, no research work has provided a consistent set of machine-interpretable traffic rules for a given operational driving domain. In this paper, we propose a methodology for the legal study and formalization of traffic rules in a formal language. We use Linear Temporal Logic as a formal specification language to describe temporal behaviors, capable of capturing a wide range of traffic rules. We contribute a formalized set of traffic rules for dual carriageways and evaluate the effectiveness of our formalized rules on a public dataset.

关键词： Law Formal languages Planning Formal specifications Monitoring Autonomous vehicles Testing

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Formalizing traffic rules for machine interpretability

arXiv

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arXiv 2020年

作者： Esterle, Klemens Gressenbuch, Luis Knoll, Alois fortiss GmbH Research Institute of the Free State of Bavaria Munich Germany Robotics Artificial Intelligence and Real-time Systems Technische Universität München Munich Germany

关键词： Specification languages

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Hierarchic Single Cluster Graph Partitioning: A Sequential Place Recognition Method

Hierarchic Single Cluster Graph Partitioning: A Sequential P...

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IEEE International Conference on systems, Man and Cybernetics

作者： Aaronkumar Ehambram Hanno Homann Sebastian P. Kleinschmidt Tobias Ritter Nicolas Fischer Bernardo Wagner Real Time Systems Group (RTS) Institute for Systems Engineering Leibniz Unversität Hannover Hannover Germany Connected Mobility and Computer Vision Systems Bosch Corporate Research Hildesheim Germany

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Localization and mapping are essential tasks in mobile robotics. A purely odometry-based pose estimation accumulates errors caused by factors such as unequal wheel-diameters or wheel-slippage and is, therefore, inaccurate. The resulting error can be corrected by recognizing previously observed places and constraining the robots' relative poses. A common challenge of such place recognition methods arises from the incorrect association of different places, which can corrupt the resulting pose correction and, consequently, any map update. Therefore, we propose a novel place recognition method that enables the selection of a correct place recognition hypothesis from a set of possible *** this work, we make use of the fact that multiple robots' trajectories run parallel in many scenarios, like in warehouse corridors or on public roads. In such situations, place recognition can be regarded as a sequential task along the robots' trajectories. We study the problem of repetitive, periodic and indistinguishable landmarks for place recognition on regularly-spaced guideposts on German rural roads. By interpreting place recognition hypotheses as nodes of a k-partite graph, we introduce our novel selection method, the Hierarchic Single Cluster Graph Partitioning, that enables the robust selection of the correct hypotheses by finding the optimal path within the graph. The selected place recognition information is used to build a map incorporating multiple observations acquired from multiple vehicles.

关键词： Roads Conferences Pose estimation Trajectory Mobile robots Task analysis Cybernetics

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