摘要
作为一种新型交通工具,飞行汽车具有空中快速飞行和地面稳定行驶的优势。在当前飞行汽车路径规划的研究中,大多算法都采用空中和地面分别规划、组合叠加,这导致状态切换过程缓慢,甚至出现状态切换点频繁起降等问题。针对上述问题,本文提出了一种基于采样点状态增广的陆空一体化路径规划方法,根据飞行汽车的陆空状态和空间环境的差异,构建路径采样点分类策略,在此基础上引入陆空状态信息,通过对传统路径采样点进行状态增广,实现了飞行汽车运动过程中陆空状态的快速高效切换,缩短了路径规划的计算时间。试验结果表明,相比于组合式路径规划算法,本文提出的路径规划方法行驶路程长度降低了22.3%,路径规划求解时间减少了34.2%。相比于无状态增广的一体式路径规划算法,本文方法的路径长度降低了8.4%,运动代价减少了11.1%。
As a new type of transportation,flying cars offer the advantages of rapid aerial flight and stable ground driving.However,most existing path planning algorithms rely on separate planning strategies for aerial and ground states,and followed by their combined superposition,which results in slow state transition and leads to frequent takeoffs and landings at switching points.To address these issues,in this paper a land-air integrated path planning method based on the state augmentation of sampling points is proposed.Considering the differences in landair states and spatial environment,a classification strategy for path sampling points is established.Furthermore,land-air state information is incorporated to augment the traditional sampling points.This enables rapid and efficient transition between aerial and ground states during vehicle movement,while also reducing path planning computation time.The experimental results show that,compared to conventional combined path planning algorithms,the proposed method reduces travel distance by 22.3%and path planning time by 34.2%.Compared to stateless augmentation-based integrated planning algorithms,the proposed method achieves an 8.4%reduction in path length and an 11.1%decrease in motion cost.
作者
刘龙龙
樊伟
肖涵
张一博
徐彬
Liu Longlong;Fan Wei;Xiao Han;Zhang Yibo;Xu Bin(School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081;School of Mechatronical Engineering,Beijing Institute of Technology,Beijing 100081)
出处
《汽车工程》
北大核心
2025年第12期2303-2313,2345,共12页
Automotive Engineering
基金
国家自然科学基金(52202452)
中国博士后科学基金面上项目(2024M764121)
中央高校基本科研业务费专项(2024CX06004)资助。
关键词
飞行汽车
无人车辆
路径规划
采样点状态增广
轨迹优化
flying cars
unmanned vehicle
path planning
sampling nodes state augmentation
trajectory optimization
