Hard-constraint trajectory planners often rely on commercial solvers and demand substantial computational resources. Existing soft-constraint methods achieve faster computation, but either (1) decouple spatial and temporal optimization or (2) restrict the search space. To overcome these limitations, we introduce MIGHTY, a Hermite spline-based planner that performs spatiotemporal optimization while fully leveraging the continuous search space of a spline. In simulation, MIGHTY achieves a 9.3% reduction in computation time and a 13.1% reduction in travel time over state-of-the-art baselines, with a 100% success rate. In hardware, MIGHTY completes multiple high-speed flights up to 6.7 m/s in a cluttered static environment and long-duration flights with dynamically added obstacles.

翻译：硬约束轨迹规划器通常依赖商业求解器且需要大量计算资源。现有的软约束方法虽然实现了更快的计算速度，但存在以下两种局限：（1）将空间优化与时间优化解耦，或（2）限制搜索空间。为克服这些限制，我们提出MIGHTY——一种基于Hermite样条的规划器，可在充分挖掘样条连续搜索空间的同时实现时空联合优化。在仿真实验中，MIGHTY相较于现有最优基线方法实现了9.3%的计算时间缩减与13.1%的行程时间减少，且成功率达100%。在硬件实验中，MIGHTY在静态杂乱环境中完成了最高6.7米/秒的多程高速飞行，并在动态障碍物场景下实现了长时程持续飞行。