An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments

机译：动态环境下基于改进人工势场的路径规划算法

获取原文

获取原文并翻译 | 示例

页面导航

摘要
著录项
相似文献
相关主题

摘要

In a dynamic environment, an Unmanned Aerial Vehicle (UAV) confronts frequently with stochastic obstacles during tracking a moving target. In this paper, we proposed an improved artificial potential field based trajectory planning algorithm for UAV tracking a dynamic target. In particular, the proposed algorithm constructed a new repulsion field by coupling a directional coordination force with relative distance between UAV and target. As a result, it can effectively solve a local minimum problem in optimization on a general potential field function, without introducing unexpected collisions with stochastically moving obstacles. Simulation results verify the feasibility and effectiveness of the proposed method.

机译：在动态环境中，无人飞行器（UAV）在跟踪运动目标时经常遇到随机障碍。在本文中，我们提出了一种改进的基于人工势场的轨迹规划算法，用于无人机跟踪动态目标。特别地，所提出的算法通过将定向协调力与无人机与目标之间的相对距离耦合来构造新的排斥场。结果，它可以有效地解决一般电位场函数优化中的局部极小问题，而不会引入与随机移动障碍物产生的意外碰撞。仿真结果验证了该方法的可行性和有效性。

著录项

来源
《2017 International Conference on Security, Pattern Analysis, and Cybernetics》|2017年|591-596|共6页
会议地点 Shenzhen(CN)
作者
Shoufeng Chen; Zhihua Yang; Zhentao Liu; Haojie Jin;
展开▼
作者单位

Department of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen, China;

Department of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen, China;

Department of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen, China;

Department of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen, China;

展开▼
会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Path planning; Dynamics; Force; Heuristic algorithms; Unmanned aerial vehicles; Target tracking; Optimization;

机译：路径规划;动力学;力;启发式算法;无人机;目标跟踪;优化;;

相似文献

外文文献
中文文献
专利

1. The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations [J] . Sang Hongqiang, You Yusong, Sun Xiujun, Ocean Engineering . 2021,第Mara1期

机译：基于改进的A *和人工势域的混合路径规划算法，用于无人面车辆结构
2. Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment [J] . Tang Jun, Sun Jiayi, Lu Cong, Proceedings of the Institution of Mechanical Engineers . 2019,第16期

机译：优化的人工势场算法在三维环境下的多无人机协同弹道规划与防撞
3. An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning [J] . BaiLi, Li-gangGong, Wen-lunYang ScientificWorldJournal . 2014,第3期

机译：一种改进的基于余量演化策略的无人战斗飞行飞行器路径规划的改进的人工蜂殖民算法
4. An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments [C] . Shoufeng Chen, Zhihua Yang, Zhentao Liu, International Conference on Security, Pattern Analysis, and Cybernetics . 2017

机译：一种改进的动态环境中无人机飞行器的基于人工势场路径规划算法
5. Artificial Potential Field Based Real-time Obstacle-free Motion Planning for Unmanned Aerial Vehicle [D] . Yin, Hang . 2020

机译：基于人工潜在领域的无人空中车辆的实时障碍运动规划
6. An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning [O] . Bai Li, Li-gang Gong, Wen-lun Yang -1

机译：基于平衡进化策略的改进人工蜂群算法在无人战斗机路径规划中的应用
7. An Improved Artificial Potential Field for Unmanned Aerial Vehicles Path Planning [O] . Lei WANG, Bing-jie LI, Zhong-hai YIN, 2017

机译：无人机航空公司路径规划改进的人工潜在领域
8. Multi-Objective Path Planning for a Team of Unmanned Aerial Vehicles (UAVs) in a Dynamic and Uncertain Environment [R] . Mishra, M, Han, X, Ayala, D F, 2014

机译：在动态和不确定环境中的无人驾驶飞行器（UaV）团队的多目标路径规划

An improved artificial potential field based path planning algorithm for unmanned aerial vehicle in dynamic environments

摘要

著录项

相似文献

相关主题

期刊订阅