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A path planning algorithm Based on A Heuristic Method

JOURNAL OF INTERNET TECHNOLOGY 2025年第2期26卷 183-198页

作者： Yan, Xiao-Zhen Zhou, Xin-Yue Ding, Ruo-Chen Luo, Qing-Hua Ju, Chun-Yu Harbin Inst Technol WeiHai Sch Informat Sci & Engn Weihai Peoples R China Shandong Inst Shipbldg Technol Jinan Peoples R China

The path planning algorithm plays a pivotal role in the field of intelligent robotics. However, in certain scenarios, the A-star algorithm exhibits drawbacks such as excessive redundant nodes and prolonged path lengths. Consequently, this paper introduces a path planning algorithm employing a heuristic approach, which will be briefly elucidated in the subsequent sections. If the straight-line path from the starting point to the destination is unobstructed, iterative calculations for path planning are unnecessary. Should the straight-line path intersect obstacles, the proposed heuristic algorithm is employed for local path planning to circumvent obstacles, and the obtained path is subsequently integrated with the overall trajectory. In the same context, we compare this algorithm with other path planning algorithms, revealing that the enhanced algorithm presented in this paper achieves a reduction in path length this study.

关键词： A-star algorithm path planning algorithm Adaptive obstacle size Heuristic method

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XGBoost-Based Heuristic path planning algorithm for Large Scale Air-Rail Intermodal Networks

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INVENTIONS 2025年第2期10卷 27-27页

作者： Weng, Shengyuan Shan, Xinghua Bai, Guangdong Wu, Jinfei Zhao, Nan China Acad Railway Sci Postgrad Dept Beijing 100081 Peoples R China China Acad Railway Sci Corp Ltd Inst Comp Technol Beijing 100081 Peoples R China

It is particularly important to develop efficient air-rail intermodal path planning methods for making full use of the advantages of air-rail intermodal networks and providing passengers with richer and more reasonable travel options. A Time-Expanded Graph (TEG) is used to model the timetable information of public transportation providing a theoretical basis for public transportation path planning. However, if the TEG includes a large amount of data such as train stations, airports, train and air schedules, the network scale will become very large, making path planning extremely time-consuming. This study proposes an XGBoost-based heuristic path planning algorithm (XGB-HPPA) for large scale air-rail intermodal networks, which use the XGBoost model to predict transfer stations before path planning, and quickly eliminate unreasonable transfer edges by adding a heuristic factor, reducing the network scale, thus accelerating the computation speed. Comparative results indicate that XGB-HPPA can markedly enhance computational speed within large-scale networks, while obtaining as many valid solutions as possible and approximating the optimal solution.

关键词： path planning algorithm XGBoost intermodal transportation air-rail transportation network heuristics algorithm

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The use of path planning algorithm using edge computing technology in college football training

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2022年第2-3期13卷 146-153页

作者： Zhou, Jun Zheng, Yingying NingboTech Univ Qixin Coll Ningbo Zhejiang Peoples R China Wenzhou Univ Coll Phys Educ & Hlth Wenzhou Zhejiang Peoples R China

The purpose is to study the role and influence of football robots on the actual football training in the current popularity of football robot competition. The theoretical basis and key technologies of the football robot system are deeply studied. The design of the wireless communication subsystem, vision subsystem, decision subsystem and fuzzy PID (port ID) control system of the football robot is completed. The application of edge computing in wireless communication subsystem is discussed. The Immune algorithm (IA), Particle Swarm Optimisation (PSO) algorithm and Immune PSO (IPSO) algorithm are compared and analysed. IPSO algorithm is used for path planning of football robots. In the comparison of the three algorithms, the convergence stability of the IPSO algorithm is significantly higher than that of IA and PSO algorithm. This research can arouse the common concern and thinking of the scientific and technological circles and the traditional football circles. It is a new field, and a challenge to traditional football. It plays an important role in the research and development of intelligent robot technology and traditional football training, especially the college football team.

关键词： edge computing path planning algorithm football vision system football training

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UAV path planning algorithm Based on Improved Rapidly-Exploring Random Tree 3

UAV Path Planning Algorithm Based on Improved Rapidly-Explor...

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3rd International Conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Wang, Renjie Yang, Ping Liu, Zejian Zhou, Dezong Li, Wensheng School of Electric Power South China University of Technology Guangdong Key Laboratory of Clean Energy Technology Guangzhou China Shenzhen Huagong Energy Technology Co. Ltd Shenzhen China Southern Offshore Wind Power United Development Co. Ltd Zhuhai China China Southern Power Grid Technology Co. Ltd Guangzhou China

ISBN: (纸本)9798350316674

In this paper, a UAV path planning algorithm based on improved rapidly-exploring random tree(RRT) is proposed to solve the problem of high randomness of sampling points and poor smoothness of planned path in the RRT path planning algorithm. In the process of tree expansion,the detection field is developed to limit the generation range of sampling points, and the generation strategy of sampling points is changed, so that the algorithm has the ability to achieve faster convergence to target and round obstacles. In addition, the improved algorithm also cuts the redundant points of the initial track, and uses the B-spline curve method to improve the smoothness of the track. The simulation results show that the UAV path planning algorithm based on the improved RRT reduces the average path length by 21.75%, the average number of tree nodes by 75.78%, and the average calculation time by 48.04% compared with the traditional RRT UAV path planning algorithm. © 2023 IEEE.

关键词： path planning algorithm rapidly-exploring random tree(RRT) UAV

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Research on Intelligent Traffic Dynamic path planning algorithm Based on ArcGIS 24

Research on Intelligent Traffic Dynamic Path Planning Algori...

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8th International Conference on Big Data and Internet of Things

作者： Chen, Jia Zhang, Feizhou Shi, Jing Lv, Hongyan Tsinghua Univ Inst Transportat Engn Beijing Peoples R China China Commun Asset Management Co Ltd Beijing Peoples R China Peking Univ Inst Remote Sensing & GIS Beijing Peoples R China Guangzhou Guojiao Runwan Transportat Informat Co Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798400717529

The purpose of path planning algorithms is to study the shortest path between two points in a topological network. Many scholars at domestic and overseas have conducted in-depth research on this topic, and there are many mature algorithms available. However, the application of path planning in-vehicle navigation systems often focuses on classical static path planning, which cannot cope with large-scale and dynamically changing traffic networks. This paper takes Geographic Information Systems (GIS) as the starting point and uses the ArcGIS traffic network model as the theoretical basis to implement various classical path planning algorithms such as PSP, Dijkstra, DBFS, L-dequeue, L-2queue, improved Dijkstra, A*, and A* bidirectional search. Experimental results show that the algorithm can provide dynamic path planning results based on real-time traffic flow changes, with a running speed nearly an order of magnitude faster than the A* bidirectional search algorithm and nearly five times faster than the ArcGIS path planning algorithm. This is of great significance for optimizing the allocation of road network resources and solving the increasingly serious urban traffic problems.

关键词： Intelligent transportation path planning algorithm Dynamic Bidirectional layering ArcGIS

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A hybrid sampling-based RRT* path planning algorithm for autonomous mobile robot navigation

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Ganesan, Sivasankar Ramalingam, Balakrishnan Mohan, Rajesh Elara Amrita Vishwa Vidyapeetham Dept Mech Engn Amrita Sch Engn Coimbatore India Vellore Inst Technol Sch Elect Engn Chennai India Singapore Univ Technol & Design Engn Prod Dev Singapore Singapore

The path-planning algorithms for autonomous mobile robot navigation are crucial, often relying on sampling- based methods. RRT* is a robust, sampling-based path planning algorithm. The sampling process in RRT* plays a pivotal role, where uniform sampling can lead to slow convergence, while non-uniform sampling offers faster convergence but may struggle in complex environments due to its limited exploration. Thus, achieving a balance between exploitation and exploration is essential when selecting the sampling method for the RRT* path-planning algorithm. To address this issue, this research paper introduces Hybrid-RRT*, a path planning method that utilizes hybrid sampling. This unique approach generates samples using both non-uniform and uniform samplers. Hybrid-RRT* is evaluated against three baseline path planning algorithms-RRT*-N, Informed RRT*, and RRT*-across three different 384x384 2D simulation environments. Compared to these baseline methods, Hybrid-RRT* achieves superior results across all five performance metrics: convergence rate, success rate, number of nodes visited, path length, and planning time. According to the numerical results, the proposed algorithm achieves a faster average convergence rate that is 76.14% higher than RRT*, 24% higher than Informed RRT*, and 3.33% higher than RRT*-N. Moreover, it reduces node exploration by an average of 48.53% compared to RRT* and 40.83% compared to Informed RRT*. The simulation results demonstrate that the proposed Hybrid-RRT* algorithm effectively addresses the issue of slow convergence with uniform sampling and the challenge of limited exploration with non-uniform sampling methods.

关键词： Hybrid sampling path planning algorithm RRT Autonomous mobile robot navigation

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Energy management strategy of intelligent plug-in split hybrid electric vehicle based on deep reinforcement learning with optimized path planning algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2021年第14期235卷 3287-3298页

作者： Xiong, Shengguang Zhang, Yishi Wu, Chaozhong Chen, Zhijun Peng, Jiankun Zhang, Mingyang Wuhan Univ Technol Intelligent Transportat Syst Res Ctr 1178 Heping Ave Wuhan 430000 Peoples R China Wuhan Univ Technol Sch Automot Engn Wuhan Peoples R China Wuhan Univ Technol Sch Management Wuhan Peoples R China Southeast Univ Sch Transportat Nanjing Peoples R China Aalto Univ Sch Engn Dept Mech Engn Marine Technol Espoo Finland

Energy management is a fundamental task and challenge of plug-in split hybrid electric vehicle (PSHEV) research field because of the complicated powertrain and variable driving conditions. Motivated by the foresight of intelligent vehicle and the breakthroughs of deep reinforcement learning framework, an energy management strategy of intelligent plug-in split hybrid electric vehicle (IPSHEV) based on optimized Dijkstra's path planning algorithm (ODA) and reinforcement learning Deep-Q-Network (DQN) is proposed to cope with the challenge. Firstly, a gray model is used to predict the traffic congestion of each road and the length of each road calculated in the traditional Dijkstra's algorithm (DA) is modified for path planning. Secondly, on the basis of the predicted velocity of each road, the planned velocity is constrained by the vehicle dynamics to ensure the driving security. Finally, the planning information is inputted to DQN to control the working mode of IPSHEV, so as to achieve energy saving of the vehicle. The simulation results show the optimized path planning algorithm and proposed energy management strategy is feasible and effective.

关键词： Intelligent hybrid electric vehicle reinforcement learning energy management path planning algorithm

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Light weight materials based vehicle secure path planning algorithm for multi constrained QoS routing VANETS 12

Light weight materials based vehicle secure path planning al...

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12th National Web Conference on Recent Advancements in Biomedical Engineering (NCRABE)

作者： Dileepkumar, R. Nagasri, B. Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Sch Engn Dept Informat Technol Chennai Tamil Nadu India

As the population is get increases the lightweight materials based vehicles are also get increases. When the people wants to move from the once place to another place they use the transport. As the number of vehicles increases the accident are happening day to day in different place. So the safety in the road is decreases. This paper proposes the VANET'S method which is the vehicle and hoc routing. This method can sends the information about the traffic congestion, road path etc to the mobile network. By using the routing method the safety is increased. In transmitting the data the traffic may happen in the message passing path. Due to this traffic in the routing system a novel path data planning is implemented. The traffic may affect the QOS (Quality of security). So the message transmission is made within a period of time. And this proposed system the supports security measure over the data path. This system is more helpful to the people and it solves a current problem which is taking place. This system is mainly depends on the social problem. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 12th National Conference on Recent Advancements in Biomedical Engineering.

关键词： Lightweight materials Multi-constrained path planning algorithm Reliable routing Vehicular ad hoc networks

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Dual-Arm path-planning algorithm for Wiring Harness Assembly Using Redundantly Actuated Robotic Systems

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IEEE ACCESS 2023年 11卷 98427-98435页

作者： Kim, Jiyoung Kim, Jin-Gyun Park, Jongwoo Han, Byung-Kil Kim, Sanghyun Park, Dong Il Korea Inst Machinery & Mat Dept Robot & Mechatron Yongin 17058 South Korea Kyung Hee Univ Dept Mech Engn Yongin 17104 South Korea

The most challenging task in the wiring harness assembly process is the placement and connection of cables on harness boards. This task remains difficult to automate because it requires dual-arm manipulation to collaboratively grasp the harnesses. In this study, we propose a dual-arm manipulation system and a path-planning framework to automate the wiring harness task. The proposed framework enables the cooperative operation of dual-arm manipulator and plans efficient paths, while considering the kinematic constraints and obstacle avoidance conditions. The framework obtains paths in two steps: 1) the configuration of the robot arms is computed using an inverse kinematics solver;and 2) when the distance between obstacles and robots falls below a certain threshold, a sampling-based algorithm plans paths that satisfy the kinematic constraints and obstacle avoidance conditions. The merits of the framework include a significant reduction in the task time compared to existing methods. This was achieved by efficiently exploring the workspace within the constrained conditions using the proposed algorithm instead of determining the path-planning conditions throughout the entire workspace. The effectiveness of the proposed framework was validated through simulations using redundantly actuated robots with multiple DoF.

关键词： Robots Collision avoidance Task analysis Robot kinematics Kinematics path planning Multi-robot systems Manipulator dynamics cooperative manipulation kinematic constraints dual-arm manipulator path planning algorithm redundantly actuated robotic systems wiring harness assembly

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Development of a Low-Cost 3D Mapping Technology with 2D LIDAR for path planning Based on the A* algorithm 6th

Development of a Low-Cost 3D Mapping Technology with 2D LIDA...

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6th Iberian Robotics Conference (Robot)

作者： Ferreira, Edilson Grilo, Vinicius Braun, Joao Santos, Murillo Pereira, Ana I. Costa, Paulo Lima, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300253 Braganca Portugal Ctr Fed Educ Tecnol Minas Gerais CEFET MG Belo Horizonte MG Brazil Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Regioes M Campus Santa Apolonia P-5300253 Braganca Portugal INESC TEC Robot & Intelligent Syst Res Grp P-4200465 Porto Portugal Univ Porto FEUP Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (纸本)9783031586750;9783031586767

This article presents the development of a low-cost 3D mapping technology for trajectory planning using a 2D LiDAR and a stepper motor. The research covers the design and implementation of a circuit board to connect and control all components, including the LiDAR and motor. In addition, a 3D printed support structure was developed to connect the LiDAR to the motor shaft. System data acquisition and processing are addressed, as well as the generation of the point cloud and the application of the A* algorithm for trajectory planning. Experimental results demonstrate the effectiveness and feasibility of the proposed technology for low-cost 3D mapping and trajectory planning applications.

关键词： path planning algorithm A LiDAR

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