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

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.

I. Introduction

Compared with ground mobile robots, multirotor aerial vehicles (MAVs) possess stronger maneuvering capability, providing them a distinct advantage effectively within confined dynamic environments. Although many related works are available [1], [2], the real-time MAV trajectory planning in confined environments with pedestrians is still a challenging problem that needs to be convincingly solved. Existing methods for handling dynamic objects involve collision checks of the instantaneous state of moving objects, known as reactive planning [3], [4]. Although this approach can enable robots to circumvent uniformly moving low-speed obstacles, it is less effective for objects with varying speeds. Delayed recognition of speed changes frequently causes the robot to approach obstacles too closely, hindering its ability to make necessary adjustments in time and increasing the risk of collisions. The critical problem of reactive planning is its lack of predictive capabilities for future positions of obstacles, which necessitates high performance for both obstacle perception and frequent trajectory replanning of the MAV [5], [6].