Evidence map›Paper›PMID 40218713›Full record

ArticleSensors (Basel, Switzerland)2025

Density-Based Detection Rapid Exploration Random Tree for Multirobot Formation Cooperative Path Planning.

Yuzhuo Shi, Yang Yang, Jinjun Liu, Kun Hao, Jiale Zhao, Haoyi Chai

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yuzhuo ShiCollege of Information Technology, Tianjin College of Commerce, Tianjin 300350, China.
Yang YangSchool of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China.
Jinjun LiuSchool of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, China.
Kun HaoSchool of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China.
Jiale ZhaoSchool of Information and Communication Engineering, Hainan University, Haikou 570228, China.
Haoyi ChaiSchool of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China.

Funding

National Natural Science Foundation of China 61902273.
6 · The paper itself

Abstract

This paper proposes a multirobot formation path planning method based on the leader-follower formation control method to ensure smooth operation in the multirobot formation control area. First, on the basis of the rapidly exploring random tree (RRT), a density detection rapidly exploring random tree (DDRRT) algorithm is designed to avoid repeated exploration of by the RRT, to quickly generate a global path from the starting point to the destination for the leader robot, and to propose a rope shrinkage path optimization mechanism for path optimization. Second, the repulsion field function in the artificial potential field (APF) is optimized for local collaborative obstacle avoidance to enable multiple robots, and a rotational potential field is introduced to solve the problems of unreachable targets and local oscillations. Finally, a control law based on consistency control is used to control the followers and introduce a formation change mechanism based on polar coordinate transformation to enhance the formation control capability. The simulation results show that the proposed strategy can provide high-quality paths for robot formations in multiple obstacle areas and guide robot formations to avoid various local obstacles quickly through formation transformation.

Indexed as

artificial potential field methodconsistency controldensity testingmultirobot formationRRT algorithm

Identifiers

PMID40218713
PMCPMC11991052

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.