In this article, a learning-based trajectory generation framework is proposed for quadrotors, which guarantees real-time, efficient, and practice-reliable navigation by online making human-like decisions via reinforcement learning (RL) and imitation learning (IL). Specifically, inspired by human driving behavior and the perception range of sensors, a real-time local planner is designed by combining learning and optimization techniques, where the smooth and flexible trajectories are online planned efficiently in the observable area. In particular, the key problems in the framework, temporal optimality (time allocation), and spatial optimality (trajectory distribution) are solved by designing an RL policy, which provides human-like commands in real-time (e.g., slower or faster) to achieve better navigation, instead of generating traditional low-level motions. In this manner, real-time trajectories are calculated using convex optimization according to the efficient and accurate decisions of the RL policy. In addition, to improve generalization performance and to accelerate the training, an expert policy and IL are employed in the framework. Compared with existing works, the kernel contribution is to design a real-time practice-oriented intelligent trajectory generation framework for quadrotors, where human-like decision-making and model-based optimization are integrated to plan high-quality trajectories. The results of comparative experiments in known and unknown environments illustrate the superior performance of the proposed trajectory generation strategy in terms of efficiency, smoothness, and flexibility.

中文翻译：

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一种新的基于学习的四旋翼轨迹生成策略。

在本文中，提出了一种基于学习的四旋翼轨迹生成框架，该框架通过强化学习 (RL) 和模仿学习 (IL) 在线做出类似人类的决策，从而保证实时、高效和实践可靠的导航。具体而言，受人类驾驶行为和传感器感知范围的启发，结合学习和优化技术设计了一个实时局部规划器，在可观察区域内高效地在线规划平滑灵活的轨迹。特别是，框架中的关键问题，时间最优性（时间分配）和空间最优性（轨迹分布）是通过设计一个 RL 策略来解决的，该策略实时提供类似人类的命令（例如，更慢或更快）以实现更好的导航，而不是生成传统的低级运动。以这种方式，根据 RL 策略的有效和准确决策，使用凸优化计算实时轨迹。此外，为了提高泛化性能并加速训练，框架中采用了专家策略和 IL。与现有工作相比，核心贡献是设计了一个面向实时实践的四旋翼智能轨迹生成框架，将类人决策和基于模型的优化相结合，规划出高质量的轨迹。在已知和未知环境中的比较实验结果说明了所提出的轨迹生成策略在效率、平滑性和灵活性方面的优越性能。根据 RL 策略的有效和准确决策，使用凸优化计算实时轨迹。此外，为了提高泛化性能并加速训练，框架中采用了专家策略和 IL。与现有工作相比，核心贡献是设计了一个面向实时实践的四旋翼智能轨迹生成框架，将类人决策和基于模型的优化相结合，规划出高质量的轨迹。在已知和未知环境中的比较实验结果说明了所提出的轨迹生成策略在效率、平滑性和灵活性方面的优越性能。根据 RL 策略的有效和准确决策，使用凸优化计算实时轨迹。此外，为了提高泛化性能并加速训练，框架中采用了专家策略和 IL。与现有工作相比，核心贡献是设计了一个面向实时实践的四旋翼智能轨迹生成框架，将类人决策和基于模型的优化相结合，规划出高质量的轨迹。在已知和未知环境中的比较实验结果说明了所提出的轨迹生成策略在效率、平滑性和灵活性方面的优越性能。