PA-MPPI: Perception-Aware Model Predictive Path Integral Control for Quadrotor Navigation in Unknown Environments

Quadrotor navigation in unknown environments is critical for practical missions such as search-and-rescue. Solving it 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 that solves 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 only optimizes trajectories in a small neighbourhood around a reference trajectory, as it lacks the ability to explore unknown regions and plan alternative paths when blocked by large obstacles. To solve this issue, we introduce Perception-Aware MPPI (PA-MPPI). Here, perception-awareness is characterized by planning and adapting the trajectory online based on perception objectives. Specifically, when the goal is occluded, PA-MPPI's perception cost biases trajectories that can perceive 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 SOTA quadrotor navigation planner for unknown environments in our challenging test scenarios. In addition, we demonstrate that PA-MPPI can be used as a safe and robust action policy for navigation foundation models, which often provide goal poses that are not directly reachable.