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

Abstract

<p>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. This paper reports on first results of an adaptive deep meta-learning energy-aware path planner 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 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, we compare the method with alternative state-of-the-art deep learning solutions. In this way, we show indications of its improved robustness to provide more informed energy estimations and energy-efficient paths when navigating over challenging uneven terrains.</p>

Probabilistic Meta-Conv1D Driving Energy Prediction for Mobile Robots in Unstructured Terrains

“…1b). For brevity of the paper, the details are referred to [15]. In this paper, we set the voxel discretization, trajectory length, and the Conv1D architecture to be the same as in [15].…”

“…Meta-learning is concerned with learning algorithms that can efficiently adapt to new tasks. In line with [15], we exploit the meta-learning black-box approach that can be formally expressed as:…”

“…Hence, a small number of local geometry-energy measurements D tr are exploited to implicitly retrieve the unknown terramechanical characteristics of the terrain and the information is leveraged to adapt the energy predictions accordingly. While in [15], single-point energy predictions were performed, in this paper, our goal is to explicitly model the probability distribution in Eq. ( 2) through neural network training.…”

“…The probabilistic Meta-Conv1D neural network architecture. In light green, the parts that differ from [15] and that have different definitions for each of the four considered distribution models.…”

“…Algorithm 1 Probabilistic black-box meta-learning training procedure. In red, differences from [15]. Randomly sample K (x, y) tr ∼ D j 6:…”