Análise e implementação de um algoritmo de sensação de movimento em uma plataforma de 2 graus de liberdade para treinamento de operadores de caminhões fora de estrada

Abstract

Vehicle simulators in mining environments play an increasingly vital role in the development and training of operators, offering high safety, reliability, low cost, and the ability to create scenarios that would be difficult to reproduce under real conditions. However, due to spatial constraints, simulators cannot fully replicate the movements of real vehicles, especially off-highway trucks like the CAT793F, which exhibit distinct behaviors in acceleration, braking, turning, and other maneuvers. This work presents a methodology for implementing a washout-type Motion Cueing Algorithm (MCA) on a motion platform with two degrees of freedom (DoF), designed to simulate pitch and roll movements consistent with the operating conditions of mining trucks. The proposed algorithm was compared with an alternative approach and showed promising results, with better motion representation, lower response delay, and reduced system-induced noise. The evaluation of the results was conducted in three main stages: initially through an objective analysis using the OMCT (Objective Motion Cueing Test), followed by a comparison with the expected response of the human vestibular system, highlighting a good similarity, especially in the semicircular canals. Finally, the influences of noise and disturbances observed in the platform’s response were considered, with these deviations being strongly associated with dynamic effects resulting from the lack of anchoring of the base to the ground. This condition favors undesired displacements and imbalances during motion, directly affecting the accuracy of the simulation and the stability of the system.