Minimum-time navigation within constrained and dynamic environments is of special relevance in robotics. Seeking time-optimality, while guaranteeing the integrity of time-varying spatial bounds, is an appealing trade-off for agile vehicles, such as quadrotors. State of the art approaches, either assume bounds to be static and generate time-optimal trajectories offline, or compromise time-optimality for constraint satisfaction. Leveraging nonlinear model predictive control and a path parametric reformulation of the quadrotor model, we present a real-time control that approximates time-optimal behavior and remains within dynamic corridors. The efficacy of the approach is evaluated according to simulated results, showing itself capable of performing extremely aggressive maneuvers as well as stop-and-go and backward motions.

翻译：限制和动态环境中的最小时间导航在机器人中具有特殊意义。 寻求时间最佳性,在保证时间变化的空间界限的完整性的同时,对于诸如四重奏器等灵活机动车辆来说,是一种具有吸引力的权衡取舍。 最先进的方法,要么承担固定的界限,产生时间最佳离线轨道,要么妥协时间最佳性以达到约束性满意度。 利用非线性模型的预测控制以及夸德罗模型的路径参数重塑,我们提出了一个实时控制,它接近时间变化的最佳行为,并且仍然留在动态走廊内。 这种方法的功效根据模拟结果进行评估,显示它有能力进行极具攻击性的动作以及中转和后移运动。