论文标题
基于运动原语的导航计划使用深层碰撞预测
Motion Primitives-based Navigation Planning using Deep Collision Prediction
论文作者
论文摘要
本文贡献了一种设计一种新型导航规划师，利用基于学习的碰撞预测网络的方法。仅考虑到机器人的当前深度图像以及机器人的估计线性和角速度，神经网络的任务是预测机器人速度 - 驱动角度空间中预定义运动原始库中每个动作序列的碰撞成本。此外，我们通过使用Monte Carlo辍学来利用无心变换和神经网络模型的不确定性来解释机器人部分状态的不确定性。然后将不确定性感知的碰撞成本与全球规划师给出的目标方向相结合，以确定以退缩的方式执行的最佳动作顺序。为了演示该方法，我们开发了一个有弹性的小型飞行机器人，该机器人整合了轻量级感测和计算资源。在混乱和感知挑战的环境中，一组模拟和实验研究（包括现场部署）都可以评估预测网络的质量以及所提出的计划者的性能。
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.
