Evidence map›Paper›PMID 40218527›Full record

ArticleSensors (Basel, Switzerland)2025

Autonomous Navigation of Mobile Robots: A Hierarchical Planning-Control Framework with Integrated DWA and MPC.

Zhongrui Wang, Shuting Wang, Yuanlong Xie, Tifan Xiong, Chao Wang

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. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

5 authors.

Zhongrui WangSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0009-0005-5023-5240
Shuting WangSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Yuanlong XieSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0000-0003-1158-1587
Tifan XiongSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Chao WangState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150080, China.ORCID 0000-0001-7398-1127

Funding

Key Research and Development Program of 494 Hubei Province, China 2022BAA064National Natural Science Foundation of China 52275488National Natural Science Foundation of China U21A20151State Key Laboratory of Robotics and Systems SKLRS-2024-KF-10
6 · The paper itself

Abstract

In human-robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning-control framework. The proposed framework explicitly incorporates path tracking constraints during path generation while simultaneously considering path characteristics in the control process. The framework comprises two principal components: (1) an enhanced Dynamic Window Approach (DWA) for the local path planning module, introducing adaptive sub-goal selection method and improved path evaluation functions; and (2) a modified Model Predictive Control (MPC) for the path tracking module, with a curvature-based reference state online changing strategy. Comprehensive simulation and real-world experiments demonstrate the framework's operational advantages over conventional methods.

Indexed as

Dynamic Window Approachmobile robotModel Predictive Controlpath planningtracking control

Identifiers

PMID40218527
PMCPMC11991454

What OpenQuestion holds

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Registered trials

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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.