Meta-Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Terrains

Abstract

Driving energy consumption plays a major role in the navigation of autonomous mobile robots in off-road scenarios. However, real-time constraints often limit the accuracy of the energy estimations, especially in scenarios where accurate wheel-terrain interactions are complex to model. In this paper, an adaptive deep meta-learning energy-aware path planner is proposed that can provide energy estimates of a mobile robot traversing complex uneven terrains with varying and unknown terrain properties. A novel feature of the method is the integration into the meta-learning framework of a 1D convolutional neural network to analyze the terrain sequentially, in the same temporal order as it would be experienced by the robot when moving, and efficiently adapt its energy estimates to the local terrain conditions based on a small number of local measurements. The performance of the method is assessed in a realistic 3D-body dynamic simulator over several typologies of deformable terrains and unstructured geometries. We provide evidence of the benefit of the proposed approach to retain 83 % r2 score of the original simulator at 0.55% of the computing time. Finally, the method is compared with alternative state-of-the-art deep learning solutions. In this way, we show indications of its improved robustness to provide more informed driving energy estimations and energy-efficient paths when navigating over challenging uneven terrains.