PANTHER: Perception-Aware Trajectory Planner in Dynamic Environments

TL;DR

PANTHER is a real-time perception-aware trajectory planner for multirotor UAVs that enhances obstacle avoidance and tracking by optimizing UAV orientation and position, significantly improving success rates in dynamic environments.

Contribution

It introduces a novel perception-aware planning approach that jointly optimizes UAV rotation and translation using differential flatness and Hopf fibration for real-time dynamic obstacle avoidance.

Findings

Keeps obstacles in FOV 7.9 and 1.5 times more effectively than non-PA methods.

Reduces projected velocity and blur by 18% and 34%.

Achieves three times higher success rates in multi-obstacle avoidance scenarios.

Abstract

This paper presents PANTHER, a real-time perception-aware (PA) trajectory planner for multirotor-UAVs (Unmanned Aerial Vehicles) in dynamic environments. PANTHER plans trajectories that avoid dynamic obstacles while also keeping them in the sensor field of view (FOV) and minimizing the blur to aid in object tracking. The rotation and translation of the UAV are jointly optimized, which allows PANTHER to fully exploit the differential flatness of multirotors to maximize the PA objective. Real-time performance is achieved by implicitly imposing the underactuated dynamics of the UAV through the Hopf fibration. PANTHER is able to keep the obstacles inside the FOV 7.9 and 1.5 times more than non-PA approaches and PA approaches that decouple translation and yaw, respectively. The projected velocity (and hence the blur) is reduced by 18% and 34%, respectively. This leads to average success rates three times larger than state-of-the-art approaches in multi-obstacle avoidance scenarios. The MINVO basis is used to impose low-conservative collision avoidance constraints in position and velocity space. Finally, extensive hardware experiments in unknown dynamic environments with all the computation running onboard are presented, with velocities of up to 5.8 m/s, and with relative velocities (with respect to the obstacles) of up to 6.3 m/s. The only sensors used are an IMU, a forward-facing depth camera, and a downward-facing monocular camera.