This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. Furthermore, we account for the uncertainty of the robot's partial state by utilizing the Unscented Transform and the uncertainty of the neural network model by using Monte Carlo dropout. The uncertainty-aware collision cost is then combined with the goal direction given by a global planner in order to determine the best action sequence to execute in a receding horizon manner. To demonstrate the method, we develop a resilient small flying robot integrating lightweight sensing and computing resources. A set of simulation and experimental studies, including a field deployment, in both cluttered and perceptually-challenging environments is conducted to evaluate the quality of the prediction network and the performance of the proposed planner.

翻译：本文提供了一种方法来设计一个新的导航规划师,利用一个基于学习的碰撞预测网络。神经网络的任务是预测机器人速度轴空间中一个预先定义的移动原始图书馆中每个动作序列的碰撞成本,仅考虑到目前的深度图像以及机器人估计的线性速度和角性速度。此外,我们通过使用蒙泰卡洛辍学,利用无色变换和神经网络模型的不确定性来说明机器人部分状态的不确定性。然后,将不确定性-觉醒碰撞成本与全球规划师给出的目标方向结合起来,以确定以后退的地平线方式执行的最佳行动顺序。为了展示这一方法,我们开发了一种具有弹性的小型飞行机器人,将光量感感和计算资源结合起来。在包罗和感知性强的环境下进行了一系列模拟和实验研究,包括实地部署,以评估预测网络的质量和拟议规划师的性能。