In many robotic tasks, such as drone racing, the goal is to travel through a set of waypoints as fast as possible. A key challenge for this task is planning the minimum-time trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance. The resulting solutions are either highly specialized for a single-track layout, or suboptimal due to simplifying assumptions about the platform dynamics. In this work, a new approach to minimum-time trajectory generation for quadrotors is presented. Leveraging deep reinforcement learning and relative gate observations, this approach can adaptively compute near-time-optimal trajectories for random track layouts. Our method exhibits a significant computational advantage over approaches based on trajectory optimization for non-trivial track configurations. The proposed approach is evaluated on a set of race tracks in simulation and the real world, achieving speeds of up to 17 m/s with a physical quadrotor.
翻译:在许多机器人任务中,如无人机赛车,目标是尽可能快地通过一套路径点进行旅行。这一任务的一个关键挑战是如何规划最短时间轨迹,通常通过假设对预通过路径点的绝对知识来解决这个问题。 由此产生的解决方案要么高度专门用于单轨布局,要么由于对平台动态的假设的简化而不够优化。 在这项工作中,提出了一种为四轨轨道生成最短时间轨迹的新方法。 利用深加学习和相对门观测,这一方法可以适应性地计算随机轨迹布局的近时最佳轨迹。 我们的方法显示,基于非三轨轨道配置的轨迹优化方法,在计算方法上有很大的优势。 在模拟和真实世界的一组种族轨迹上,对拟议方法进行了评估,通过物理四轨图实现高达17米/秒的速度。