Fast Exploration Planning with Learning-Based Motion Time Prediction for Aerial Robots

Abstract

Unmanned aerial vehicles (UAVs) have been widely employed to achieve autonomous exploration of 3D unknown environments. However, most existing algorithms suffer from low exploration efficiency caused by inaccurate motion time cost evaluation, which typically leads to the motion inconsistency during the UAV flight. In this work, we propose a learning-based motion time prediction method for real-time evaluating the accurate motion time costs to candidate viewpoints. Specifically, the prediction method takes the current state of the UAV and its surrounding environment features as input to predict the arrival time to each viewpoint. Based on the motion time cost prediction, the UAV can minimize the time wasted by unnecessary acceleration and deceleration during exploration. To further improve the efficiency, we also develop an optimal exploration target decision algorithm that benefits from the predicted motion time costs and the adaptive upper-bound constraints. Simulation and real-world experiments demonstrate that our method can significantly improve the exploration efficiency and increase the average flight speed of the UAV.