An autonomous navigation system with a trajectory prediction-based decision mechanism for rubber forest navigation.

Xirui Zhang, Yongqi Liu, Junxiao Liu, Xuanli Chen, Ruiwu Xu, Weiqiang Ma, Zhifu Zhang, Shaohua Fu

Abstract read

In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Xirui ZhangSchool of Mechanical and Electrical Engineering, Hainan University, Hainan, China.

Yongqi LiuSchool of Mechanical and Electrical Engineering, Hainan University, Hainan, China.

Junxiao LiuSchool of Mechanical and Electrical Engineering, Hainan University, Hainan, China.

Xuanli ChenSchool of Mechanical and Electrical Engineering, Hainan University, Hainan, China.

Ruiwu XuSchool of Information and Communication Engineering, Hainan University, Hainan, China.

Weiqiang MaSchool of Mechanical and Electrical Engineering, Hainan University, Hainan, China.

Zhifu ZhangSchool of Mechanical and Electrical Engineering, Hainan University, Hainan, China. 996099@hainanu.edu.cn.

Shaohua FuSchool of Information and Communication Engineering, Hainan University, Hainan, China. 22120854000009@hainanu.edu.cn.

Funding

Hainan Province Academician Innovation Platform Research YSPTZX202109Hainan Provincial Science and Technology Talent Innovation Project KJRC2023C04Hainan University Collaborative Innovation Center Research Project XTCX2022NYB05International Scientific and Technological Cooperation R&D Project in Hainan Province GHYF2023002National Natural Science Foundation of China U23A20176

6 · The paper itself

Abstract

The use of rubber-tapping robots capable of autonomous navigation in place of manual rubber-tapping is a growing trend, but the challenging multi-objective navigation task in forest environments impedes their autonomous operation. To tackle this issue, an autonomous navigation system with a trajectory prediction-based decision mechanism for rubber forest navigation is designed. This navigation decision mechanism is comprised of obtaining coordinates of target points (OCTP), selecting the next coordinate (SNC), generating the additional coordinates (GAC), and optimizing the planned paths (OPP). By utilizing this mechanism, the robot can autonomously select the next target point based on its current position and the actual operating logic while navigating in the forest areas, adding additional coordinates during row or column changes, and planning and optimizing the path. The on-site experiments demonstrate that during autonomous navigation, the positioning accuracy is favorable and supports subsequent operations. The overall rationality of the planned path reaches 92.14%, further confirming its effectiveness.

Indexed as

Autonomous navigation systemMulti-objective navigationNavigation decision mechanismRubber-tapping robot

Identifiers

PMID39604535

DOI10.1038/s41598-024-81084-9

PMCPMC11603044

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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.