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%.
翻译:在具有挑战性的环境中,驾驶能源消耗在移动机器人的导航中起着重要作用,特别是如果这些机器人只能靠有限的机载动力在有限时间内操作,则更是如此。本文报告了一个能见路径规划员的第一批结果,该结果可以提供机器人穿越复杂不均地形的驱动能源消耗和能源回收的估计。能源是利用自我监督的学习方法,在轨迹上估计的,机器人自主学习如何将所觉察到的地形点云与能源消耗和回收联系起来。该方法的一个新特点是,使用1D脉冲神经网络,按机器人在移动时将经历的同一时间顺序对地形进行顺序分析。在模拟从真实的自然情景中收集的若干数字地形模型时,对拟议方法的性能进行评估,并与一个超自然偏重的能源模型进行比较。我们证明,我们的方法有利于将总预测R2分提高66.8%,并将计划路径的驱动能源消耗减少5.5%。