Predicting Energy Consumption and Traversal Time of Ground Robots for Outdoor Navigation on Multiple Types of Terrain

TL;DR

This paper presents a data-driven machine learning approach using ResNet to predict energy consumption and traversal time for outdoor ground robot navigation across diverse terrains, improving path planning accuracy.

Contribution

It introduces a novel ResNet-based model trained on field data to accurately estimate navigation costs for various terrains, enhancing outdoor robot path planning.

Findings

Prediction error within 11% of ground truth data

Model outperforms existing baseline methods

Effective across multiple terrain types

Abstract

The outdoor navigation capabilities of ground robots have improved significantly in recent years, opening up new potential applications in a variety of settings. Cost-based representations of the environment are frequently used in the path planning domain to obtain an optimized path based on various objectives, such as traversal time or energy consumption. However, obtaining such cost representations is still cumbersome, particularly in outdoor settings with diverse terrain types and slope angles. In this paper, we address this problem by using a data-driven approach to develop a cost representation for various outdoor terrain types that supports two optimization objectives, namely energy consumption and traversal time. We train a supervised machine learning model whose inputs consists of extracted environment data along a path and whose outputs are the predicted energy consumption and traversal time. The model is based on a ResNet neural network architecture and trained using field-recorded data. The error of the proposed method on different types of terrain is within 11\% of the ground truth data. To show that it performs and generalizes better than currently existing approaches on various types of terrain, a comparison to a baseline method is made.