Artificial neural network to predict traction performance of autonomous ground vehicle on a sloped soil bin and uncertainty analysis

Abstract

• ANN models were developed to estimate the AGV’s traction behavior on slopes. • Uncertainty analysis quantified the reliability and robustness of ANN model output. • ANN is a promising, robust, and reliable method to predict off-road vehicle’s traction performance. • Predictive traction models would empower the multi-AGV’s operation on a high slope. A fleet of autonomous ground vehicles (AGV) is envisioned to expand farming to arable land suitable for production except for being too steep for conventional equipment. The success of proposed multi-AGV system largely depends on the traction performance of the individual AGVs on unevenly sloped terrain and optimization of the AGVs control variables. Therefore, the drawbar pull performance of a prototype AGV was evaluated in a soil bin at varying slopes, speeds, and drawbar pull (DP). The AGV’s traction performance was expressed in three metrics: tractive efficiency (TE), travel reduction ratio (TRR), and power number (PN). Optimizing the control variables is intricate and ill-defined, which requires an accurate model to predict the performance of the proposed multi-AGV system. Hence, this study aims to design an artificial neural network (ANN) to estimate the traction behavior of the AGV on a sloped testbed as a function of AGV’s speed, applied DP, and slope. A multi-layer perceptron feed-forward ANN architecture with a single hidden layer trained with a back-propagation algorithm was adopted. A series of ANN models with increasing complexity and different hidden layer activation functions were developed for each response variable, i.e., ANN-TE, ANN-TRR, and ANN-PN. A re-sampling-based method, K-fold cross-validation, was employed to estimate the model generalization error. The model success was evaluated via Mean Squared Error (MSE) and the Coefficient of Determination ( R 2 ) against a test set. The final predictive model was trained on the entire data set, and the observed R 2 was 0.933, 0.882 and 0.858, respectively, for ANN-TE, ANN-TRR, and ANN-PN. Subsequently, a Monte-Carlo Simulation based uncertainty analysis was carried out to demonstrate the model strength and the degree of uncertainty by constructing a 95% prediction interval. This study shows ANN as a promising, robust, and reliable method to predict traction performance in agricultural tillage-traction studies and developed models can empower the multi-AGV system on steep-uneven slope terrain.