A State-Time Space Approach for Local Trajectory Replanning of an MAV in Dynamic Indoor Environments

Abstract

Multirotor aerial vehicles (MAVs) in confined, dynamic indoor environments need reliable planning capabilities to avoid moving pedestrians. Current MAV trajectory planning algorithms often result in low success rates or unnecessary constraints on navigable space. We propose a multi-stage local trajectory planner that predicts pedestrian movements using State-Time Space (ST-space) based on the Euclidean Signed Distance Field (ESDF) to tackle these challenges. Our method quickly generates collision-free trajectories by incorporating spatiotemporal optimization and fast ESDF queries. Based on statistical analysis, our method improves performance over state-of-the-art MAV trajectory planning methods as pedestrian speed increases. Finally, we validate the real-time applicability of our proposed method in indoor dynamic scenarios.