The Local Path Planning Algorithm for Drones Based on Multi-Perspective and Multi-Resolution

SHANG Song; LIU Ming-gang; DING Shi-qiang

doi:10.3969/j.issn.2096-6091.2023.14.003

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The Local Path Planning Algorithm for Drones Based on Multi-Perspective and Multi-Resolution

RESEARCH PAPER | 更新时间：2025-03-26

- The Local Path Planning Algorithm for Drones Based on Multi-Perspective and Multi-Resolution
- New Generation of Information Technology Vol. 6, Issue 14, Pages: 9-12(2023)
- 作者机构：
  
  海军航空大学，山东烟台 264001
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.14.003
  CLC： V249.122+.6;V249.4
- Accepted：2023-08-10，
  
  Published：30 July 2023
- 稿件说明：
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尚嵩, 刘明刚, 丁士强. 基于多层视野的无人机局部路径规划算法[J]. 新一代信息技术, 2023, 6(14): 09-12

SHANG Song, LIU Ming-gang, DING Shi-qiang. The local path planning algorithm for drones based on multi-perspective and multi-resolution[J]. New Generation of Information Technology, 2023, 6(14): 09-12
尚嵩, 刘明刚, 丁士强. 基于多层视野的无人机局部路径规划算法[J]. 新一代信息技术, 2023, 6(14): 09-12 DOI： 10.3969/j.issn.2096-6091.2023.14.003.

SHANG Song, LIU Ming-gang, DING Shi-qiang. The local path planning algorithm for drones based on multi-perspective and multi-resolution[J]. New Generation of Information Technology, 2023, 6(14): 09-12 DOI： 10.3969/j.issn.2096-6091.2023.14.003.

摘要

局部路径规划是无人机的环境智能感知系统中核心的技术之一。本文分析了当前应用广泛的基于固定活动窗的局部规划方法，针对该方法的环境适应性问题提出了多层视野多分辨率局部路径规划算法。其核心思路是参照区域障碍物密度指标对无人机的视野范围进行分层，根据视野的环境复杂度动态确定需要使用的视野层次和各层次分辨率，然后划分扇区综合评价扇区的可通行程度，同时对相邻可通行扇区合并，减少候选方向数量，再利用修正的代价函数综合分析候选方向，最终得到无人机的局部前进避障规划。

Abstract

Local path planning is a crucial component of the environmental intelligent sensing system used by unmanned aerial vehicles (UAVs). This paper presents a novel algorithm for local path planning that addresses the environmental adaptability issue associated with traditional methods. The key idea is to stratify the UAV's field of view and to dynamically determine the levels of field of view to be used and the resolution of each level based on the environmental complexity of the field of view. In addition

regions are evaluated according to the density of obstacles

and candidate directions are determined. Finally

the candidate directions are comprehensively analyzed using a modified cost function

and the UAV's local forward obstacle avoidance planning is obtained. This algorithm demonstrates the ability of UAVs to adapt to changing environmental conditions

which is essential for effective navigation and obstacle avoidance. The use of multi-perspective and multi-resolution approaches enables more accurate assessments of the environment and increased maneuverability for UAVs. Ultimately

this research has the potential to improve UAV operations in a wide range of applications

including search and rescue

mapping and surveying

and military operations.

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Keywords

references

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