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A novel fuzzy inference method for urban incomplete road weight assignment

地球空间信息科学学报（英文版） 2024年第6期27卷中插15,2008-2022页

作者： Longhao Wang Xiaoping Rui College of Hydrology and Water Resources Hohai UniversityNanjingChina Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina School of Earth Science and Engineering Hohai UniversityNanjingChina

One of the keys in time-dependent routing is determining the weight of each road network link based on traffic *** facilitate the estimation of the road's weight,Global Position System(GPS)data are commonly used in obtaining real-time traffic ***,the information obtained by taxi-GPS does not cover the entire road *** at incom-plete traffic information on urban roads,this paper proposes a novel fuzzy inference *** considers the combined effect of road grade,traffic information,and other spatial *** the third law of geography as the basic premise,that is,the more similar the geogra-phical environment,the more similar the characteristics of the geographical target will *** method uses a Typical Link Pattern(TLP)model to describe the geographical *** TLP represents typical road sections with complete ***,it determines the relationship between roads lacking traffic information and the TLPs according to their related *** obtaining the TLPs,this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy *** at road links at different places,the dividing-conquering strategy and globe algorithm are also introduced to calculate the *** two strategies are used to address the excessively fragmented or lengthy *** experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error(RMSE)is 1.430 mph,and the bias is 0.2％;the overall RMSE is 11.067 mph,and the bias is 0.6％.This article is the first to combine the third law of geography with fuzzy inference,which significantly improves the estimation accuracy of road weights with incomplete *** application and validation show that the method can accurately predict vehicle speed under incomplete information.

关键词： Weight assignment path planning algorithm fuzzy inference road network

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A path planning algorithm Based on Fusing Lane and Obstacle Map

A Path Planning Algorithm Based on Fusing Lane and Obstacle ...

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IEEE International Conference on Intelligent Transportation Systems

作者： Hao Zhu Mengyin Fu Yi Yang Xinyu Wang Meiling Wang Sch. of Autom. Beijing Inst. of Technol. Beijing China

ISBN: (纸本)9781479960798

This paper proposes a path planning algorithm for autonomous driving in urban environments. The processing of video and Velodyne pointcloud provides information about the positions of lane markers and obstacles in the local map, which are then converted to a lane costmap and obstacle costmap. The referenced GIS follow line is used for generating a series of offset curves, and the best follow line is selected according to a combination of lane, obstacle and background cost. Additional handling of planning path and maximum speed is provided. Our planning algorithm can handle various road types such as U-turn, intersections, and different driving behaviors including passing over or following front vehicles, etc. The proposed navigation framework is implemented on an autonomous vehicle, which exhibits good performance on Future Challenge 2013, Changshu, China.

关键词： geographic information systems image fusion mobile robots path planning road vehicles robot vision video signal processing Velodyne pointcloud autonomous driving autonomous vehicle following front vehicles fusing lane lane costmap obstacle costmap obstacle map path planning algorithm referenced GIS follow line urban environments video processing Cameras Laser radar Mobile robots planning Roads Turning Vehicles

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Tool path Optimization for Complex Cavity Milling Based on Reinforcement Learning Approach

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IEEE ACCESS 2023年 11卷 66793-66807页

作者： Wan, Yi Xu, Wei Zuo, Tian-Yu Nanjing Xiaozhuang Univ Sch Environm Sci Nanjing 211171 Peoples R China Sanjiang Univ Sch Mech & Elect Engn Nanjing 210012 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China

In the machining of parts, tool paths for complex cavity milling often have different generation options, as opposed to simple machining features. The different tool path generation options influence the machining time and cost of the part during the machining process. Decision makers prefer tool path solutions that have fewer blanking lengths, which means that the machining process is more efficient. Therefore, in order to reduce costs and increase efficiency, it is necessary to carefully design the tool path generation for the features to be machined on the part, especially for complex cavity milling features. However, solutions to the problem of optimal design of tool paths for complex cavity milling features have not been well developed in current research work. In this paper, we present a systematic solution for complex cavity milling tool path generation based on reinforcement learning. First, a grid converter is executed for converting the 3D geometry of the cavity milling feature into a matrix of planar grid points recognisable by the program, set according to the cutting parameters. Afterwards, the tool path generation process is refined and modelled as a Markov decision process. Ultimately, a tool path generation solution combining the A* algorithm with the Q-learning algorithm is executed. The agent iterates through trial and error to construct an optimal tool path for a given cavity milling task. Three case experiments demonstrate the feasibility of the proposed approach. The superiority of the reinforcement learning-based approach in terms of solution speed and solution quality is further demonstrated by comparing the proposed approach with the evolutionary computational techniques currently popular in research for solving tool path optimisation design problems.

关键词： Tool path optimization cavity milling path planning algorithm reinforcement learning Q-learning algorithm

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A Rapidly-Exploring Random Tree algorithm with Reduced Random Map Size 9

A Rapidly-Exploring Random Tree Algorithm with Reduced Rando...

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9th International Conference on Automation, Robotics and Applications (ICARA)

作者： Lonklang, Aphilak Botzheim, Janos Eotvos Lorand Univ Dept Artificial Intelligence Fac Informat H-1117 Budapest Hungary

ISBN: (纸本)9781665489218

Mobile robots have been widely used in automated factory applications such as raw material delivery and product storage transportation. path planning algorithms have been proposed to generate a feasible global approach. The path result must be free from obstacle regions and shortest. Previously we proposed an Improved Rapidly-Exploring Random Tree (Improved RRT*) algorithm. The algorithm consists of the pre-processing step for feasible mapping, primary processing with RRT* for path generating, and post-processing with Bacterial Mutation and Node Deletion operators. This paper aims to improve further the capability of the Improved RRT* algorithm by reducing the overall computation time. The proposed method reduces the complexity of the random map after each iteration by deleting the used nodes. In this way, the computational time could be reduced.

关键词： path planning algorithm RRT Bacterial Mutation Node Deletion

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Improved value iteration network for path planning

Improved value iteration network for path planning

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作者： Shijia Chai Department of Electronic Engineering Tsinghua University

In this study,a series of improved path planning algorithms are designed for path planning tasks in autonomous control based on deep reinforcement *** Value Iteration Network(VIN) is used to deal with the path planning *** VIN performs well on small size maps,but when it comes to a bigger size of map on test set,the success rate *** order to solve the problem that origin VIN lacks long-distance multi-step planning ability on large maps and generalization ability is insufficient,a three-step improvement was *** of all,in view of the inconvenient data flow and the disappearance of gradients caused by the network being too deep,the jump connection structure is used to obtain the deeper VIN,in which the accuracy of the experiment is ***,with the purpose of solving the problem that the complexity of the model is greatly increased due to the deepening of the network,Batch normalization is used to obtain a new network with dueling architecture plus batch normalization layer,which further accelerates the convergence speed of the ***,to deal with the global path planning problem on the big map,the hierarchical network structure is adopted for hierarchical value iteration,and the Hierarchical Structure VIN is *** Hierarchical Structure VIN,the long-term planning ability and generalization ability of the algorithm have been significantly improved,and the algorithm could figure out the large-scale and complex path planning problem.

关键词： path planning algorithm deep reinforcement learning deep learning hierarchical structure

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UGV Optimal path planning algorithm by using 3D Survivability Map

UGV Optimal Path Planning Algorithm by using 3D Survivabilit...

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10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Kim, Min-Ho Lee, Min-Cheol Pusan Natl Univ Pusan South Korea

ISBN: (纸本)9781479911974;9781479911950

UGV has widely used in the battlefield, thus UGV's survivability technique has become one of the main issue on the path planning algorithm. In this study, 3D survivability map is suggested for UGV survivability, and the optimal path planning algorithm on this map is also suggested. 3D survivability map is a grid map on which the 3D terrain data is combined with the battle field data, and each grid node on the map has UGV's survival probability value. The suggested path planning algorithm finds the optimal path by comparing the survival probability value of the grid nodes. At last, the simulation program has been developed, and the results are shown to verify the suggested algorithm.

关键词： UGV 3D Survival Probability path planning algorithm

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Homotopy path planning for Terrestrial Robots Using Spherical algorithm

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2018年第2期15卷 567-585页

作者： Diaz-Arango, Gerardo Vazquez-Leal, Hector Hernandez-Martinez, Luis Sanz Pascual, Maria Teresa Sandoval-Hernandez, Mario Natl Inst Astrophys Opt & Elect Elect Dept Puebla 72840 Mexico Univ Veracruzana Elect Instrumentat & Atmospher Sci Sch Xalapa 91000 Veracruz Mexico Univ Xalapa Ciencia Cultura & Tecnol Xalapa 91190 Veracruz Mexico Ctr Bachillerato Tecnol Ind & Serv Veracruz 91777 Veracruz Mexico

Autonomous and semiautonomous mobile robots play an important role in making tasks in environments considered hostile or dangerous for a human being. In order to execute many of the required tasks, robots need, in its architecture, a module of navigation with an appropriate path planning algorithm. This paper presents the development and implementation of a methodology for path planning of a mobile robot using a spherical algorithm and homotopy continuation methods (HCMs). The first section is a brief introduction about HCMs. Subsequently, the homotopy path planning method and spherical path tracking algorithm are explained, as well as the upgraded version and its main features. Then, the main contributions of this paper are presented and the effectiveness of the proposed method is proved. Besides, numerical examples, implementation results in multiple platforms including a 32b microcontroller and robot operating system, are displayed. Finally, some favorable results from a comparative between the proposed methodology against sampling-based planners using the open motion planning library are presented.

关键词： Homotopy continuation methods (HCMs) mobile robotics open motion planning library path planning algorithm robot operating system (ROS) spherical path tracking algorithm

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A Study of path planning algorithm Based on the Survival Probability

A Study of Path Planning Algorithm Based on the Survival Pro...

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12th International Conference of Intelligent Autonomous Systems

作者： Kim, Min-Ho Noh, Chi-Beom Heo, Jung-Hun Lee, Min-Cheol Pusan Natl Univ Dept Mech Engn Pusan 609735 South Korea

ISBN: (纸本)9783642339257

After the development of fusion technology of mechanics, electronics and IT, there are a lot of researches about the autonomous vehicle. For this kind of vehicle to move automatically, it needs the reference input that the vehicle can follow, and this reference input comes from a path planning algorithm. A* is one of the well-known global path planning algorithm that finds the optimal path on the given map using the heuristic cost function. However, A* algorithm gives the vehicle some reference points on the path not the continuous reference path. And A* may not guarantee the path will not collide the corner of the obstacle. To overcome this problem, in this paper, the hybrid path planning algorithm is suggested using B-spline equation and new heuristic cost function with survival probability. At last, we developed the simulation program and the results are shown.

关键词： autonomous vehicle A* algorithm B-spline curve equation path planning algorithm

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Research on a multiobjective cooperative operation scheduling method for agricultural machinery across regions with time windows

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 224卷

作者： Guo, Yaqian Zhang, Fan Chang, Shuhui Li, Zheng Li, Zikang Hebei Agr Univ Coll Informat Sci & Technol Baoding 071000 Peoples R China Hebei Agr Univ Hebei Key Lab Agr Big Data Baoding 071000 Peoples R China

This article aims to solve the problems of high cost, low efficiency, and task delay caused by the lack of scientific scheduling strategies during the agricultural harvest season. Considering the constraints of operation time windows, a multimachine multiobjective cross-regional collaborative operation scheduling model is constructed with the goal of minimizing the transfer distance of agricultural machinery and nonoperational scheduling costs. A multiobjective genetic algorithm (HTSMOGA) based on a hybrid time window priority rule and a tabu search strategy is designed. The initial population is generated by utilizing time window priority rules, genes with earlier operation start times are preferentially retained, and a mixed search strategy is introduced to avoid local optimal solutions. Experiments were conducted among 24 farms in a region of Hebei Province, and the results showed that, compared with other algorithms, the HTSMOGA achieved an average reduction of 17.18 % in the transfer distance of agricultural machine operations and 19.36 % in the nonoperational waiting time. Therefore, this study provides a rational and feasible solution for the cross-regional operation scheduling of agricultural machinery cooperatives and provides theoretical support for ensuring the timely completion of harvesting tasks and the cost savings and efficiency of agricultural machinery operations.

关键词： Time window priority rule Hybrid tabu search strategy Multiobjective optimization Cooperative work path planning algorithm

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UGV obstacle detection of sensor fusion based on point cloud data segmentation 7

UGV obstacle detection of sensor fusion based on point cloud...

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7th Asia Pacific Conference on Optics Manufacture (APCOM)

作者： Yu Xin Fan Yimei Xu Nana Chen Jinqi Chen Tianding Minnan Normal Univ Sch Comp Zhangzhou 363000 Peoples R China Minnan Normal Univ Sch Phys & Informat Engn Zhangzhou 363000 Peoples R China

ISBN: (数字)9781510652095

ISBN: (纸本)9781510652095;9781510652088

This paper uses the point cloud data extracted by LiDAR as the basis of obstacle detection and proposes an algorithm to reduce the computational complexity of point cloud. Firstly, it removes the points too high or too low from the point cloud data, leaving only the points forming the obstacle interval. Secondly, the point cloud in the interval is projected to the X-Y plane (horizontal plane) and the resolution is reduced by lattice. Finally, the connected component marker clustering method is used to segment and classify obstacles. After identifying the obstacle, the scene information is provided to the path planning algorithm. In the path planning algorithm, Unmanned Ground Vehicle (UGV) moves along the shortest straight line connecting the target point and the starting point. When encountering obstacles, the edge tracking method is used to bypass the obstacle, and then it goes straight again. After planning by the path planning algorithm, UGV can avoid collision and advance to the destination in real time through the motion control system. The experimental result shows that UGV designed by this research has the ability to reach the target point safely, and it can avoid obstacles and move autonomously in real-time conditions and complete outdoor unknown environmental obstacle detection.

关键词： D LiDAR point cloud segmentation obstacle detection path planning algorithm collision avoidance

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