Quadrotor navigation in unknown environments is critical for practical missions such as search-and-rescue. Solving this problem requires addressing three key challenges: path planning in non-convex free space due to obstacles, satisfying quadrotor-specific dynamics and objectives, and exploring unknown regions to expand the map. Recently, the Model Predictive Path Integral (MPPI) method has emerged as a promising solution to the first two challenges. By leveraging sampling-based optimization, it can effectively handle non-convex free space while directly optimizing over the full quadrotor dynamics, enabling the inclusion of quadrotor-specific costs such as energy consumption. However, MPPI has been limited to tracking control that optimizes trajectories only within a small neighborhood around a reference trajectory, as it lacks the ability to explore unknown regions and plan alternative paths when blocked by large obstacles. To address this limitation, we introduce Perception-Aware MPPI (PA-MPPI). In this approach, perception-awareness is characterized by planning and adapting the trajectory online based on perception objectives. Specifically, when the goal is occluded, PA-MPPI incorporates a perception cost that biases trajectories toward those that can observe unknown regions. This expands the mapped traversable space and increases the likelihood of finding alternative paths to the goal. Through hardware experiments, we demonstrate that PA-MPPI, running at 50 Hz, performs on par with the state-of-the-art quadrotor navigation planner for unknown environments in challenging test scenarios. Furthermore, we show that PA-MPPI can serve as a safe and robust action policy for navigation foundation models, which often provide goal poses that are not directly reachable.

翻译：在未知环境中进行四旋翼导航对于搜救等实际任务至关重要。解决此问题需要应对三个关键挑战：由障碍物导致的非凸自由空间中的路径规划、满足四旋翼特定的动力学和目标，以及探索未知区域以扩展地图。最近，模型预测路径积分（MPPI）方法已成为解决前两个挑战的一种有前景的方案。通过利用基于采样的优化，MPPI 能够有效处理非凸自由空间，同时直接在完整的四旋翼动力学上进行优化，从而能够纳入诸如能耗等四旋翼特定的成本。然而，MPPI 一直局限于跟踪控制，仅在参考轨迹周围的小邻域内优化轨迹，因为它缺乏探索未知区域以及在遇到大型障碍物阻塞时规划替代路径的能力。为了克服这一局限，我们提出了感知感知 MPPI（PA-MPPI）。在该方法中，感知感知的特征在于基于感知目标在线规划和调整轨迹。具体来说，当目标被遮挡时，PA-MPPI 引入了一个感知成本，该成本使轨迹偏向于那些能够观测未知区域的轨迹。这扩展了已映射的可通行空间，并增加了找到通往目标的替代路径的可能性。通过硬件实验，我们证明，在具有挑战性的测试场景中，以 50 Hz 频率运行的 PA-MPPI 与最先进的未知环境四旋翼导航规划器性能相当。此外，我们还展示了 PA-MPPI 可以作为导航基础模型的安全且鲁棒的动作策略，这些模型通常提供的目标位姿并非直接可达。