Perception and Localization Error Propagation and Robust Path-Tracking Control for Agricultural Machinery in Hilly Orchards: A Review.

Navigating autonomous farm machines in hilly orchards is tough because the ground is uneven, tree canopies block sensors, and the wheels can slip. These conditions cause large errors in the machine’s perception and positioning systems, and the errors spread through the control loop, making it hard to follow a planned path accurately. This project offers a clear review and a step‑by‑step framework that focuses on two things: – How perception and localization errors build up on slopes. – How to design robust path‑tracking controllers. The study looks at sensor mistakes from GNSS, LiDAR, and inertial measurement units (IMUs) on inclined terrain and shows how to model the error growth. It

also examines strong control methods—sliding mode control and model predictive control—that can counteract slip and slope‑related disturbances. The framework helps B.Tech students create a simulated or small‑scale prototype of a path‑tracking controller. The prototype should combine sensor‑fusion algorithms to reduce localization drift. By carefully analyzing sensor noise and vehicle dynamics, the resource supports the design of more reliable guidance systems for autonomous orchard machines working on difficult terrain.

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