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。现有的软约束方法实现了更快的计算速度，但要么（1）解耦空间与时间优化，要么（2）限制搜索空间。为克服这些局限，本文提出MIGHTY——一种基于埃尔米特样条的规划器，该规划器在执行时空优化的同时，能充分利用样条的连续搜索空间。仿真结果表明，相较于最先进的基线方法，MIGHTY在保持100%成功率的同时，实现了9.3%的计算时间缩减与13.1%的行程时间缩减。在硬件实验中，MIGHTY在杂乱静态环境中完成了最高6.7米/秒的多组高速飞行，并在动态添加障碍物的场景下实现了长时程飞行。