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2017 2nd International Conference on Test, Measurement and Computational Method(TMCM 2017)

作者： Tian-tian ZHAO Si-ming WANG School of Automation and Electrical Engineering Lanzhou Jiaotong University

Based on the artificial potential field algorithm and bacterial foraging algorithm, research method for obstacle avoidance and path planning of the mobile robot. Firstly, analysis method of bacterial foraging three operations: trends, replication, migration, establishment of bacterial foraging algorithm process;secondly establish the bacterial foraging algorithm model;and then analyze the artificial potential field algorithm and bacterial foraging the advantage and disadvantage of the algorithm, proposed a new hybrid algorithm has the global search ability and fast convergence. Using artificial potential field and bacterial foraging method on mobile robot global path planning and then compared with the mixture algorithm, based on the simulation results, the global path planning of mobile robot Simulation results show the feasibility and effectiveness of the hybrid algorithm.

关键词： artificial potential field algorithm Bacterial foraging algorithm Path planning robot

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A Hybrid Path Planning Method Based on Improved A* and CSA-APF algorithms

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IEEE ACCESS 2024年 12卷 39139-39151页

作者： Gao, Ju Xu, Xiangrong Pu, Quancheng Petrovic, Petar B. Rodic, Aleksandar Wang, Zhixiong Anhui Univ Technol Sch Mech Engn Maanshan 243032 Peoples R China Univ Belgrade Fac Mech Engn Belgrade 11120 Serbia Univ Belgrade Inst Mihajlo Pupin Belgrade 11060 Serbia Osaka Univ Sch Med Osaka 5650871 Japan

In order to address the challenges of global path planning in complex and dynamic environments, where avoiding dynamic obstacles is difficult, and local paths are prone to getting stuck in local optima, this paper proposes an enhanced hybrid path planning approach based on improvements to the A* algorithm and the artificial potential field (APF) algorithm. Firstly, addressing the issues of redundant path nodes and non-smooth paths generated by the A* algorithm, we introduce enhancements such as weighted heuristic function, removal of redundant path nodes, and the incorporation of a cubic quasi-uniform B-spline curve to improve search speed and generate smoother paths. Secondly, addressing the problems of unreachable target points and susceptibility to local optima associated with the APF algorithm, two key strategies are employed. On one hand, the repulsive potential field function is modified by incorporating the m-th power of the relative distance from the robot to the target point to ensure reachability. On the other hand, the CSA-APF (Constrained Simulated Annealing with Augmented potential field) algorithm is introduced. This algorithm combines the APF algorithm with Simulated Annealing (SA), incorporating constraints on angles and safety distances to escape local optima and generate globally optimal points. Finally, the proposed methods are compared through simulation experiments in different scenarios. The experimental results demonstrate that the proposed approach effectively achieves path planning in complex dynamic environments.

关键词： Mobile robots Path planning A* algorithm artificial potential field algorithm

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Hybrid Path Planning Model for Multiple Robots Considering Obstacle Avoidance

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IEEE ACCESS 2022年 10卷 71914-71935页

作者： Zhang, Tianrui Xu, Jianan Wu, Baoku Shenyang Univ Sch Mech Engn Shenyang 110044 Peoples R China

To solve the problem that the single robot task execution capability is not enough to meet the whole handling task demand under complex conditions, the hybrid path planning models such as multi-robot path planning and formation cooperative control considering obstacle avoidance are studied. Firstly, for the robot global path finding problem, on the basis of the construction for a robot working environment model based on the geometric map model building method, an improved particle swarm algorithm-based global path planning model is proposed to solve the problems of low robot path planning solution efficiency and easy to fall into local optimal solutions. Secondly, for the multi-robot cooperative formation control and obstacle avoidance and inter-robot collision avoidance problems, a multi-robot formation local path planning model based on the improved artificial potential field method is constructed, a simulated annealing algorithm is introduced to optimize the traditional artificial potential field method, and a multi-robot formation control strategy, robot obstacle avoidance, and inter-robot collision avoidance methods are designed in combination with the pilot-following method to improve the robot formation path exploration The proposed method can improve the path exploration capability and handling efficiency of robot formation. Finally, the global path planning model of the robot based on the improved particle swarm algorithm is simulated and analyzed using Matlab 7.0 to verify the outstanding performance of the model in pathfinding capability. Then the local path planning model of multi-robot formation based on the improved artificial potential field is simulated and analyzed to verify the improved algorithm has good path planning as well as obstacle avoidance performance. The hybrid path planning model is applied to a real case and simulated, and the results show that the improved algorithm improves the exploration capability of the robot formation, effective

关键词： Robots Collision avoidance Path planning Robot kinematics Task analysis Service robots Robot sensing systems Path planning improved particle swarm algorithm environment modeling artificial potential field algorithm formation control

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LSDA-APF:A Local Obstacle Avoidance algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

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Computer Modeling in Engineering & Sciences 2024年第1期138卷 595-617页

作者： Xiaoli Li Tongtong Jiao Jinfeng Ma Dongxing Duan Shengbin Liang School of Software Henan UniversityKaifeng475004China

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local ***,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control *** algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different *** the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential *** the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the *** local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid *** objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle *** simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

关键词： Unmanned surface vehicles local obstacle avoidance algorithm artificial potential field algorithm path planning collision detection

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Research on Improved potential field Ant Colony algorithm for UAV Path Planning 33

Research on Improved Potential Field Ant Colony Algorithm fo...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Chen, Tao Lv, Xinyu Wang, Shengying Ta, Na Zhao, Jing Chen, Xinpei Xiao, Mingxia Wei, Haicheng North Minzu Univ Sch Elect & Informat Engn Yinchuan 750021 Ningxia Peoples R China Ningxia Univ Sch Informat Engn Yinchuan 750021 Ningxia Peoples R China Ningxia Presch Educ Coll Educ Informat Ctr Yinchuan 750001 Ningxia Peoples R China

ISBN: (纸本)9781665440899

In order to solve the problems of slow convergence speed and prone to local optimization in ant colony algorithm, an improved ant colony algorithm of potential field is proposed in this study. Firstly, the method of pheromone affected by artificial potential field is introduced to reduce the blindness of the ant colony. Then, the potential field heuristic information is used to accelerate the convergence speed of ant colony algorithm. Finally, the decrease coefficient of potential field resultant force is used to solve the local optimal problem. Compare with the traditional algorithm, the experiments show that the improved algorithm in this paper can get better results, in terms of path length and the number of convergence iterations, the average search time for convergence is reduced by 44.83% in the 20x20 map.

关键词： path planning ant colony algorithm artificial potential field algorithm the decrease coefficient of potential field resultant force

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Cooperative Control of Multi-Agent Systems

Cooperative Control of Multi-Agent Systems

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作者： Gevargiz, Patrick California State University Northridge

学位级别：硕士

This project proposes a simple, efficient and scalable method for motion planning and control of multi-agent systems (MAS) with collision avoidance capabilities. This report begins with the review of studies and algorithms in the fields of cooperative control of MAS and swarm robotics for collective behavior. In this project, classic artificial potential field (APF) algorithm is utilized to implement control and motion planning for autonomous robots (agents) with obstacle avoidance capabilities. The APF algorithm provides effective and smooth trajectory planning compared with other algorithms that are studied in this project. Furthermore, to address classical APF algorithm limitations such as local minima and narrow channels, a Lyapunov theorem approach, is used to optimize potential fields and resolve those limitations. In addition, a special case of modified APF algorithm is studied to implement leader-follower scenario. The results for each controller are verified using MATLAB simulation. Finally, a survey of existing hardware platform to implement MAS formation control algorithms is presented.

关键词： artificial potential field algorithm Multi Agent Systems Swarm APF

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A Disaster Relief UAV Path Planning Based on APF-IRRT* Fusion algorithm

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DRONES 2023年第5期7卷 323-323页

作者： Diao, Qifeng Zhang, Jinfeng Liu, Min Yang, Jiaxuan Jiangsu Univ Res Ctr Fluid Machinery Engn & Technol Zhenjiang 212013 Peoples R China Inst Fluid Engn Equipment JITRI Zhenjiang 212009 Peoples R China

Unmanned Aerial Vehicle (UAV) path planning has increasingly become the key research point for civilian drones to expand their use and enhance their work efficiency. Focusing on offline derivative algorithms, represented by Rapidly-exploring Random Trees (RRT), are widely utilized due to their high computational efficiency. However, deploying these offline algorithms in complex and changing disaster environments presents its own drawbacks, such as slow convergence speed, poor real-time performance, and uneven generation paths. In this paper, the artificial potential field -Improved Rapidly-exploring Random Trees (APF-IRRT*) path-planning algorithm is proposed, which is applicable to disaster relief UAV cruises. The RRT* algorithm is adapted with adaptive step size and adaptive search range coupled with the APF algorithm for final path-cutting optimization. This algorithm guarantees computational efficiency while giving the target directivity of the extended nodes. Furthermore, this algorithm achieves remarkable progress in solving problems of slow convergence speed and unsmooth path in the UAV path planning and achieves good performance in both offline static and online dynamic environment path planning.

关键词： Rapidly-exploring Random Trees algorithm artificial potential field algorithm UAV path planning

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Research on Improved potential field Ant Colony algorithm for UAV Path Planning

Research on Improved Potential Field Ant Colony Algorithm fo...

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第33届中国控制与决策会议

作者： Tao Chen Xinyu Lv Shengying Wang Na Ta Jing Zhao Xinpei Chen Mingxia Xiao Haicheng Wei School of Electrical and Information Engineering North Minzu University School of Information Engineering Ningxia University Education Information Center Ningxia Preschool Education College

In order to solve the problems of slow convergence speed and prone to local optimization in ant colony algorithm,an improved ant colony algorithm of potential field is proposed in this ***,the method of pheromone affected by artificial potential field is introduced to reduce the blindness of the ant ***,the potential field heuristic information is used to accelerate the convergence speed of ant colony ***,the decrease coefficient of potential field resultant force is used to solve the local optimal *** with the traditional algorithm,the experiments show that the improved algorithm in this paper can get better results,in terms of path length and the number of convergence iterations,the average search time for convergence is reduced by 44.83% in the 20 x20 map.

关键词： path planning ant colony algorithm artificial potential field algorithm the decrease coefficient of potential field resultant force

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基于人工势场算法的改航路径规划

基于人工势场算法的改航路径规划

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第八届全国交通运输领域青年学术会议

作者：徐肖豪李成功赵嶷飞中国民航大学空中交通管理研究基地天津300300

为解决空中交通流量管理中恶劣天气或流量受限下的航班改航问题,将人工势场算法应用于航班改航路径的规划,并考虑空中交通管制程序和飞行性能等约束条件,采用线性拟合的方法对生成初始改航路径进行分段拟合,消除振荡点和误差较大的离散... 详细信息

为解决空中交通流量管理中恶劣天气或流量受限下的航班改航问题,将人工势场算法应用于航班改航路径的规划,并考虑空中交通管制程序和飞行性能等约束条件,采用线性拟合的方法对生成初始改航路径进行分段拟合,消除振荡点和误差较大的离散点;对拟合后的航段通过截弯取直的方法,去除多余的转弯点,生成最终的改航路径.运用本文提出的方法对我国西南雷暴多发地区的改航路径进行了仿真,仿真结果表明本文提出的改航路径规划方法可安全有效地避开飞行受限区域,算法简单可行.

关键词： Air traffic management Path planning artificial potential field algorithm Reroute Linear sub-fitting

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