Evidence map›Paper›PMID 42817241›Full record

ArticleSensors (Basel, Switzerland)2026

An Adaptive Bounded Hybrid A* Planner for Autonomous Mining Trucks in Confined Unstructured Mining Environments.

Zijie Meng, Ruixin Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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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0cells of the map it votes in
0citing papers in PubMed
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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

2 authors.

Zijie MengSchool of Energy and Mining Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China.
Ruixin ZhangSchool of Energy and Mining Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automated valet-parking trajectory planning has been widely studied in structured environments, whereas its counterpart in unstructured environments remains insufficiently explored. The working face of an open-pit mine is a typical unstructured production environment, where autonomous mining trucks (AMTs) must perform collision-free and kinematically feasible maneuvers under irregular boundaries, locally confined passages, large vehicle footprints, and strict terminal pose requirements. To address this problem, we introduce an adaptive bounded Hybrid A* planner for AMT maneuver planning at mining working faces. The proposed planner integrates local-geometry-aware top-K motion primitive scheduling, adaptive rollout-length assignment, progress-reward-based dual-cost correction, and bounded continuation after the first feasible Reeds-Shepp closure. By ranking and filtering motion primitives according to obstacle clearance, maneuver margin, goal distance, and heading alignment, the planner reduces ineffective successor generation during search. By adapting rollout lengths to local spatial constraints and primitive curvature, it improves the consistency between expansion scale and environmental structure. By retaining the first feasible Reeds-Shepp connection as an initial upper bound and continuing bounded refinement, it improves the final returned path beyond the first feasible solution. Comparative studies demonstrate that, relative to the conventional Hybrid A* baseline, the proposed planner reduces the average final planning time, generated nodes, and final path cost by 54.73%, 74.10%, and 12.76%, respectively, in the abstract mine-like scenarios. In the real-map-derived mining working-face scenarios, the corresponding reductions are 25.12%, 37.98%, and 7.15%, respectively. These results indicate that the proposed method reduces search redundancy while improving the efficiency-quality trade-off in confined and geometrically irregular working-face environments.

Indexed as

adaptive expansionautonomous mining trucksbounded continued searchHybrid A*

Identifiers

PMID42817241
PMCPMC13611504

What OpenQuestion holds

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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.