Upgraded trajectory planning method deployed in autonomous exploration for unmanned aerial vehicle

Abstract

Autonomous exploration is grounded on target decision and trajectory planning, which is widely deployed on unmanned aerial vehicles. However, existing methods generally only focus on the exploration effect of target decision but neglect the environment information gained with trajectory planning during flight, resulting in redundant exploration trajectories and low exploration efficiency. This article proposes an upgraded method of trajectory planning for autonomous exploration work. We design a fresh cost term considering the frontier information in the part of trajectory optimization. Besides, yaw angles are planned independently to catch more environment information during flight. We present extensive simulations and real-world tests. The results show that our proposed method reduces the exploration cost time by 10–15% compared with the previous one.