A Generalized Voronoi Diagram-Based Efficient Heuristic Path Planning Method for RRTs in Mobile Robots

运动规划 沃罗诺图 启发式 计算机科学 特征(语言学) 任意角度路径规划 移动机器人 人工智能 算法 特征提取 数学优化 机器人 数学 几何学 语言学 哲学
作者
Wenzheng Chi,Zhiyu Ding,Jiankun Wang,Guodong Chen,Lining Sun
出处
期刊:IEEE Transactions on Industrial Electronics [Institute of Electrical and Electronics Engineers]
卷期号:69 (5): 4926-4937 被引量:110
标识
DOI:10.1109/tie.2021.3078390
摘要

The rapidly exploring random tree and its variants (RRTs) have been widely adopted as the motion planning algorithms for mobile robots. However, the trap space problem, such as mazes and S-shaped corridors, hinders their planning efficiency. In this article, we present a generalized Voronoi diagram (GVD)-based heuristic path planning algorithm to generate a heuristic path, guide the sampling process of RRTs, and further improve the motion planning efficiency of RRTs. Different from other heuristic algorithms that only work in certain environments or depend on specified parameter setting, the proposed algorithm can automatically identify the environment feature and provide a reasonable heuristic path. First, the given environment is initialized with a lightweight feature extraction from the GVD, which guarantees that any state in the free space can be connected to the feature graph without any collision. Second, to remove the redundancy of feature nodes, a feature matrix is proposed to represent connections among feature nodes and a corresponding feature node fusion technique is utilized to delete the redundant nodes. Third, based on the GVD feature matrix, a heuristic path planning algorithm is presented. This heuristic path is then used to guide the sampling process of RRTs and achieve real-time motion planning. The proposed GVD feature matrix can be also utilized to improve the efficiency of the replanning. Through a series of simulation studies and real-world implementations, it is confirmed that the proposed algorithm achieves better performance in heuristic path planning, feature extraction of free space, and real-time motion planning.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
JY完成签到,获得积分10
刚刚
小黑完成签到,获得积分10
1秒前
深情安青应助Sky采纳,获得10
1秒前
莉莉丝完成签到,获得积分20
1秒前
满意紫菜完成签到,获得积分20
1秒前
科研通AI2S应助极度采纳,获得10
1秒前
西酞普绿发布了新的文献求助10
2秒前
2秒前
2秒前
小蘑菇应助真谛采纳,获得10
4秒前
山城小肘子关注了科研通微信公众号
4秒前
SYLH应助李梓明采纳,获得10
4秒前
orixero应助土豆土豆采纳,获得10
7秒前
7秒前
牧秋妈妈完成签到,获得积分10
7秒前
8秒前
aaaaaa发布了新的文献求助10
8秒前
英姑应助苗条菠萝采纳,获得30
9秒前
9秒前
牧秋妈妈发布了新的文献求助10
11秒前
12秒前
12秒前
灰灰发布了新的文献求助10
14秒前
顺利的历发布了新的文献求助10
14秒前
15秒前
xiangdannuli发布了新的文献求助10
15秒前
16秒前
16秒前
17秒前
单薄店员发布了新的文献求助10
18秒前
19秒前
19秒前
19秒前
真谛完成签到,获得积分10
20秒前
深情安青应助清新的Q采纳,获得10
21秒前
22秒前
厘米完成签到,获得积分10
22秒前
肥而不腻的羚羊完成签到,获得积分10
23秒前
莉莉丝发布了新的文献求助10
23秒前
真谛发布了新的文献求助10
23秒前
高分求助中
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 588
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3962593
求助须知:如何正确求助?哪些是违规求助? 3508565
关于积分的说明 11141766
捐赠科研通 3241330
什么是DOI,文献DOI怎么找? 1791510
邀请新用户注册赠送积分活动 872888
科研通“疑难数据库(出版商)”最低求助积分说明 803483