Semantic-Aware Trajectory Planning for UAV in Dynamic Environments

TLDR

This work proposes a two-stage workflow that generates UAV trajectories by simultaneously leveraging semantic information and accounting for cluttered, dynamic environments, and is the first approach that generates UAV trajectories by simultaneously leveraging semantic information and accounting for cluttered, dynamic environments.

Abstract

Generating trajectories that leverage semantic information to guide a UAV safely and accurately to its destination in a dynamic environment remains an open problem. In the existing literature, semantics have been used to prioritize certain areas - either to guide the UAV through or to avoid them -for specific objectives, such as reducing errors in visual-inertial SLAM (VI-SLAM). However, prior work typically assumes a static environment when performing collision checking, even in cluttered and dynamic settings.

We propose a two-stage workflow: The first stage performs semantic-aware pathfinding. The second stage optimizes the resulting path, incorporating kinematic constraints and performing collision checking that accounts for obstacle motion, while still ensuring real-time performance.

To the best of our knowledge, this is the first approach that generates UAV trajectories by simultaneously leveraging semantic information and accounting for cluttered, dynamic environments. A summary video is available at https://youtu.be/I5w6AP7HThU