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Acoustic emission source location in complex structures based on artificial potential field-guided rapidly-exploring random tree* and genetic algorithm

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 224卷

作者： Nie, Jia-Hao Li, Dan Wang, Hao Xiang, Shu-Lin Yu, Tao Mao, Jian-Xiao Southeast Univ Key Lab C&PC Struct Minist Educ Nanjing 211189 Peoples R China Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China Hefei Gen Machinery Res Inst Hefei 230031 Peoples R China

Towards more accurate and easy-to-implement damage detection in large-scale complex structures, a novel acoustic emission (AE) source location method is developed based on artificial potential field-guided rapidly-exploring random tree* (APF-RRT*) and genetic algorithm (GA). APF-RRT*, which combines the excellent obstacle avoidance ability of RRT* with the path planning efficiency of APF, is introduced to adaptively estimate the shortest distances from the damage source to AE sensors. The shortest distances are obtained as the actual propagation distances of waves and then embedded into the modified error function, where GA is employed as an optimization scheme to evaluate the source location via iterations. Through the experiment on a full-scale high-strength bolt joint plate with a series of bolt holes, the effectiveness and superiority of the proposed method were validated. It achieved a better source location performance with lower mean absolute error and standard deviation than the time-of-arrival (TOA) method, delta-T mapping method, and machine learning-improved methods based on Gaussian process (GP) and artificial neural network (ANN), respectively. The primary contributions of the proposed method lay in abandoning the straight-wave-propagation assumption of the traditional TOA method by adaptively taking into account the geometric obstacles in complex structures, and removing the need for a large amount of training data and burdensome pencil lead break (PLB) tests required by data-driven location methods.

关键词： Acoustic emission Damage location Complex structures Artificial potential field rapidly-exploring random tree Genetic algorithm

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A Novel Direct Trajectory Planning Approach Based on Generative Adversarial Networks and rapidly-exploring random tree

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第10期23卷 17910-17921页

作者： Zhao, Cong Zhu, Yifan Du, Yuchuan Liao, Feixiong Chan, Ching-Yao Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai 201804 Peoples R China Eindhoven Univ Technol Urban Planning Grp NL-5612 AZ Eindhoven Netherlands Univ Calif Berkeley Calif PATH Berkeley CA 94804 USA

Trajectory planning is essential for self-driving vehicles and has stringent requirements for accuracy and efficiency. The existing trajectory planning methods have limitations in the feasibility of planned trajectories and computational efficiency. This paper proposes a life-long learning framework to achieve effective and high-accuracy direct trajectory planning (DTP) tasks. Based on generative adversarial networks (GANs), this study develops a lightweight GDTP model to map the initial/final states and the control action sequence. Additionally, by embedding the GDTP into the rapidly-exploring random tree (RRT), a GDTP-RRT algorithm is further designed for long-distance and multi-stage planning tasks. Taking the tractor-trailer as an application case, we test the proposed method in multiple scenarios with varying characteristics. The experimental results show that the method can plan highly feasible trajectories in a short time, compared with the most applied algorithm - the cubic curve RRT* (CCRRT*). It is found that the tracking errors of our method are 29.1% and 44.1% lower than the CCRRT* in terms of position and heading angle. This paper provides an effective and stable vehicle trajectory planning method for complex self-driving tasks.

关键词： Trajectory planning Trajectory Data models Task analysis Planning Deep learning Neural networks Self-driving vehicles trajectory planning generative adversarial networks rapidly-exploring random tree tractor-trailer

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A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning

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INTEGRATED COMPUTER-AIDED ENGINEERING 2020年第2期27卷 121-138页

作者： Perez-Hurtado, Ignacio Martinez-del-Amor, Miguel A. Zhang, Gexiang Neri, Ferrante Perez-Jimenez, Mario J. Univ Seville Sch Comp Engn Dept Comp Sci & Artificial Intelligence Res Grp Nat Comp Seville Spain Chengdu Univ Technol Coll Informat Sci & Technol Chengdu Sichuan Peoples R China Univ Nottingham Sch Comp Sci COL Lab Nottingham England

In recent years, incremental sampling-based motion planning algorithms have been widely used to solve robot motion planning problems in high-dimensional configuration spaces. In particular, the rapidly-exploring random tree (RRT) algorithm and its asymptotically-optimal counterpart called RRT* are popular algorithms used in real-life applications due to its desirable properties. Such algorithms are inherently iterative, but certain modules such as the collision-checking procedure can be parallelized providing significant speedup with respect to sequential implementations. In this paper, the RRT and RRT* algorithms have been adapted to a bioinspired computational framework called Membrane Computing whose models of computation, a.k.a. P systems, run in a non-deterministic and massively parallel way. A large number of robotic applications are currently using a variant of P systems called Enzymatic Numerical P systems (ENPS) for reactive controlling, but there is a lack of solutions for motion planning in the framework. The novel models in this work have been designed using the ENPS framework. In order to test and validate the ENPS models for RRT and RRT*, we present two ad-hoc implementations able to emulate the computation of the models using OpenMP and CUDA. Finally, we show the speedup of our solutions with respect to sequential baseline implementations. The results show a speedup up to 6x using OpenMP with 8 cores against the sequential implementation and up to 24x using CUDA against the best multi-threading configuration.

关键词： Optimal motion planning rapidly-exploring random tree Membrane Computing OpenMP CUDA

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Anytime Synchronized-Biased-Greedy rapidly-exploring random tree Path Planning in Two Dimensional Complex Environments

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2011年第4期9卷 750-758页

作者： Yang, Kwangjin Korea AF Acad Dept Aerosp & Mech Engn Ssangsoo Namil Cheongwon 363849 Chungbuk South Korea

A new synchronized biased-greedy RRT is proposed which leverages the strengths of the biased and greedy RRTs. It combines the advantage of the biased RRT that grows trees towards the goal location, with the ability of the greedy RRT that makes trees traverse the environment in a single iteration. The proposed method achieves performance improvements compared to other RRT variants, not only in computational time but also in the quality of the path. Two enhancements are made to the initial path to relax the sub-optimality of the RRT path;first a path pruning algorithm is executed to eliminate redundant nodes and an anytime strategy is adapted to continuously enhance the quality of the path within the deliberation time.

关键词： Anytime strategy obstacle avoidance path planning rapidly-exploring random tree

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Adaptive rapidly-exploring random tree for efficient path planning of high-degree-of-freedom articulated robots

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2015年第18期229卷 3361-3367页

作者： Kim, Dong-Hyung Choi, Youn-Sung Kim, Sang-Ho Wu, Jing Yuan, Chao Luo, Lu-Ping Lee, Ji Yeong Han, Chang-Soo Hanyang Univ Dept Robot Engn ERICA Campus Gyeonggi Do 425791 South Korea Hanyang Univ Dept Mech Engn Seoul 133791 South Korea Hanyang Univ Dept Mechatron Engn ERICA Campus Gyeonggi Do South Korea

This article proposes a method for the path planning of high-degree-of-freedom articulated robots with adaptive dimensionality. For efficient path planning in a high-dimensional configuration space, we first describe an adaptive body selection that selects the robot bodies depending on the complexity of the path planning. Then, the involved joints of the selected body are included in the planning process. That is, it builds the C-space (configuration space) with adaptive dimensionality for sampling-based path planner. Next, by using adaptive body selection, the adaptive rapidly-exploring random tree (RRT) algorithm is introduced, which incrementally grows RRTs in the adaptive dimensional C-space. We show through several simulation results that the proposed method is more efficient than the basic RRT-based path planner, which requires full-dimensional planning.

关键词： Path planning motion planning rapidly-exploring random tree high degree-of-freedom articulated robot

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A rapidly-exploring random tree-based intelligent congestion control through an alternate routing for WSNs

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2023年第1期29卷 71-94页

作者： Prakash, P. Suman Kavitha, D. Reddy, P. Chenna Jawaharlal Nehru Technol Univ Anantapur Anantapur Andhra Pradesh India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool India Jawaharlal Nehru Technol Univ Anantapur Dept Comp Sci & Engn Anantapur Andhra Pradesh India

In wireless sensor networks (WSNs), congestion is a challenging issue, and it degrades the efficiency in terms of packet loss, energy wastage, throughput, etc. The primary cause of the congestion in WSNs is the data routing of the many-to-one pattern. It means multiple nodes can send their data to a single sink using multi-hop transmissions. To control the congestion in WSN, we use a rapidly-exploring random tree (RRT)-based mechanism to divert the data packets from the congested nodes. Initially, we use a mathematical model to determine the congested nodes in the WSNs. Further, we identify the routing path using the RRT algorithm in which the algorithm can construct a dynamic routing while avoiding the congested nodes in the routing path. We estimate the efficiency of our approach using simulation runs and compare the results using the recently published algorithm. We notice the improved performance in our method.

关键词： wireless sensor networks WSNs congestion factor estimation intelligence congestion control rapidly-exploring random tree routing quality of service

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Progressive rapidly-exploring random tree for Global Path Planning of Robots 9

Progressive Rapidly-exploring Random Tree for Global Path Pl...

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9th International Conference on Control, Automation and Robotics (ICCAR)

作者： Tian, Miaomiao Yu, Jiyang China Acad Space Technol Inst Spacecraft Syst Engn Beijing Peoples R China

ISBN: (纸本)9798350322514

Global path planning is an essential task for autonomous robots operating on the moon. To increase the efficiency of global path planning, we propose a Progressive rapidly-exploring random tree (P-RRT) algorithm, which improves RRT in three aspects. First, to speed up the convergence rate, we proposed the Feasible Region-aware Ellipse Sampling strategy, which incrementally draws biased samples from an elliptical subspace with the current point and the target point as the focus. Second, we introduce dynamic forward step length to accelerate random tree growth. Third, given the wide range of applications for multi-core processors, we introduce parallel path planning, segmenting global path planning into several processing units, in an effort to further enhance the application efficiency of the algorithm. Finally, the proposed P-RRT is verified by extensive simulation experiments. The experimental results demonstrate the efficiency and practicability of the P-RRT algorithm.

关键词： Global path planning rapidly-exploring random tree Progressive strategy

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LOCAL TRAJECTORY PLANNING FOR AUTONOMOUS RACING VEHICLES BASED ON THE rapidly-exploring random tree ALGORITHM 23

LOCAL TRAJECTORY PLANNING FOR AUTONOMOUS RACING VEHICLES BAS...

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ASME International Design Engineering Technical Conferences / 41st Annual Computers and Information in Engineering Conference (IDETC-CIE) / 23rd International Conference on Advanced Vehicle Technologies (AVT)

作者： Tramacere, Eugenio Luciani, Sara Feraco, Stefano Circosta, Salvatore Khan, Irfan Bonfitto, Angelo Amati, Nicola Politecn Torino DIMEAS Dept Mech & Aerosp Engn Turin Italy

ISBN: (纸本)9780791885369

This paper presents a local trajectory planning method based on the rapidly-exploring random tree (RRT) algorithm using Dubins curves for autonomous racing vehicles. The purpose of the investigated method is the real-time computation of a trajectory that could be feasible in autonomous driving. The vehicle is considered as a three Degree-of-Freedom bicycle model and a Model Predictive Control (MPC) algorithm is implemented to control the lateral and longitudinal vehicle dynamics. The trajectory planning algorithm exploits a perception pipeline using a LiDAR sensor that is mounted onto the front wing of the racing vehicle. The MPC computes the acceleration/ deceleration command and the front wheel steering angle to follow the predicted trajectory. The trajectory and control algorithms are tested on real data acquisition performed on- board the vehicle. For validation purposes, the vehicle is driven autonomously during different maneuvers performed in the racing environment that is structured with traffic cones. The feasibility of the algorithm is evaluated in terms of error with respect to the planned trajectory, tracking velocity and maximum longitudinal acceleration. The effectiveness of the method is also evaluated with respect to command signals for the steering and acceleration actuators featured by the retained racing vehicle. The results demonstrate that the trajectory is well-tracked and the signals are compatible with the actuator constraints.

关键词： trajectory planning autonomous driving rapidly-exploring random tree Dubins curves

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Mobile Robot Path Planning Based on an Improved rapidly-exploring random tree in Unknown Environment

Mobile Robot Path Planning Based on an Improved Rapidly-expl...

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IEEE International Conference on Automation and Logistics

作者： Liu Chang-an Chang Jin-gang Li Guo-dong Liu Chun-yang N China Elect Power Univ Sch Comp Sci & Technol Beijing 102206 Peoples R China

ISBN: (纸本)9781424425020

To deal with the path planning of mobile robot in unknown environment, an improved rapidly-exploring random tree (RRT) is presented by analyzing basic RRT algorithm. We applied improved RRT algorithm to local navigation of a mobile robot in unknown environment. Detecting local unknown environments by robot sensor system, the method orients a biased direction from RRT algorithm is used to drive the robot to avoid obstacles and move to the goal. The method is not only simple and flexible, but also reduces the nodes of tree and the amount of calculation for real-time computation. We conducted the simulation to verify the proposed method in real unknown environment.

关键词： Mobile Robot Path Planning rapidly-exploring random tree Biased Direction

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UAV 3-Dimension Flight Path Planning Based on Improved rapidly- exploring random tree 31

UAV 3-Dimension Flight Path Planning Based on Improved Rapid...

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31st Chinese Control And Decision Conference (CCDC)

作者： Li Meng Sun Qinpeng Zhu Mengmei Univ Jinan Sch Elect Engn Jinan 250022 Peoples R China Qilu Univ Technol Shandong Prov Key Lab Automot Elect Technol Shandong Acad Sci Inst Automat Jinan 250014 Peoples R China

ISBN: (纸本)9781728101057

The traditional RRT algorithm is difficult to realize the 3-dimensional flight path planning of unmanned aerial vehicle (UAV). In view of the complexity and particularity of the 3-dimensional environment, an improved RRT algorithm is proposed. According to the specific task requirements and the performance limitation of UAV, the flight height constraint, the maximum range constraint and the pitch angle constraint are designed. A new node acceptance criteria and path adjustment method is proposed for the 3-dimensional environment. From the theoretical point of view, the convergence of the improved algorithm is analyzed. On the other hand, a 3-dimensional simulation environment is established for algorithm test. The simulation results show that the improved RRT algorithm can realize the path planning in three-dimensional environment and meet the mission requirements of UAV.

关键词： Unmanned Aerial Vehicle rapidly-exploring random tree path planning 3-dimensional Environment

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