Robust estimation of Ackerman angles for front-axle steered vehicles

Verónica Sáiz-Rubio, Francisco Rovira-Más, Ishani Chatterjee, José María Molina Hidalgo


The multiple benefits of automating steering in agricultural vehicles have resulted in various autoguidance systems commercially available, most of them relying on satellite-based positioning. However, the fact that farm equipment is typically oversized, heavy, and highly powered poses serious challenges to automation in terms of safety and reliability. The objective of this research is to improve the reliability of front-wheel feedback signals as a preliminary stage in the development of stable steering control systems. To do so, the angle turned by each front wheel of a conventional tractor was independently measured by an optical encoder and fused to generate the Ackerman feedback angle. The proposed fusion algorithm analyzes the consistency of each signal with time and checks the coherence between left and right front wheels according to the vehicle steering mechanism. Field experiments demonstrated the benefits of using redundant sensors coupled through logic algorithms for estimating Ackerman angles as the harsh conditions of off-road environments often resulted in the unreliable performance of electronic devices.

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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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