ArticleFrontiers in plant science2025
Path tracking control method for tracked agricultural vehicles based on slip-aware look-ahead point offset.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Perception and Localization Error Propagation and Robust Path-Tracking Control for Agricultural Machinery in Hilly Orchards: A Review.Sensors (Basel, Switzerland) · 2026Review
- Autonomous driving system for single hydrostatic transmission crawler tractor based on multi-modal steering control.Frontiers in plant science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Tracked agricultural vehicles operating in complex farmland environments are prone to track slip, which degrades path-tracking accuracy and may lead to unstable motion. To address the limitations of conventional geometric tracking algorithms under slip conditions, this study proposes a slip-aware look-ahead point offset path-tracking control method for tracked agricultural machinery. Methods: An extended Kalman filter (EKF) is developed to fuse RTK-IMU pose measurements with track wheel-speed feedback, enabling real-time estimation of left and right track slip ratios. Based on the estimated slip difference, a target-point offset compensation mechanism is constructed, and the offset angle is optimized online using an improved particle swarm optimization (PSO) algorithm with a Chebyshev-window-based inertia weight strategy. In addition, a fuzzy controller is employed to adaptively adjust the look-ahead distance according to vehicle speed and path curvature, while a first-order low-pass filter is applied to smooth the commanded velocities. Results: Simulation results demonstrate that the proposed method significantly reduces lateral tracking errors and maintains smooth trajectories under severe slip conditions. Field experiments conducted at speeds of 0.35 m/s and 0.75 m/s show that the proposed method reduces the maximum lateral deviation by 78.1% and the average deviation by 50.6% compared with the traditional fuzzy pure pursuit algorithm. At 0.75 m/s, the maximum and average deviations are further reduced by 63.1% and 57.6%, respectively. Discussion: The results confirm that incorporating slip estimation and slip-aware target-point offset compensation effectively enhances path-tracking accuracy and robustness for tracked agricultural vehicles operating on soft and high-slip terrain. The proposed lightweight control framework provides a practical and reliable solution for autonomous navigation and plant-protection operations in complex farmland environments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.