Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments

TL;DR

This paper introduces an energy-aware path planning method for mobile robots using a 1D CNN to predict energy consumption and recovery on uneven terrains, improving prediction accuracy and reducing energy use.

Contribution

It presents a novel 1D CNN-based approach for estimating energy consumption in terrain traversal, trained via self-supervised learning, and demonstrates its effectiveness in simulation.

Findings

Increased prediction r2 score by 66.8%.

Reduced energy consumption over planned paths by 5.5%.

Validated on real terrain models in simulation.

Abstract

Driving energy consumption plays a major role in the navigation of mobile robots in challenging environments, especially if they are left to operate unattended under limited on-board power. This paper reports on first results of an energy-aware path planner, which can provide estimates of the driving energy consumption and energy recovery of a robot traversing complex uneven terrains. Energy is estimated over trajectories making use of a self-supervised learning approach, in which the robot autonomously learns how to correlate perceived terrain point clouds to energy consumption and recovery. A novel feature of the method is the use of 1D convolutional neural network to analyse the terrain sequentially in the same temporal order as it would be experienced by the robot when moving. The performance of the proposed approach is assessed in simulation over several digital terrain models collected from real natural scenarios, and is compared with a heuristic inclination-based energy model. We show evidence of the benefit of our method to increase the overall prediction r2 score by 66.8% and to reduce the driving energy consumption over planned paths by 5.5%.