Unmanned Aerial Vehicles (UAVs) embodied intelligence is of increasingly important since UAVs are well suited to perform tasks that are dangerous or impractical for humans. Numerous model-based approaches for autonomous UAV exploration have been proposed, yet they predominantly rely on a single sensor. This reliance limits the range of information obtained and diminishes the success rate in search missions. In this paper, in order to tackle those issues, a multimodal UAV autonomous exploration system is proposed. A pioneering method is introduced that harnesses deep reinforcement learning to synergize spectral data with LiDAR information, thereby significantly enhancing the navigation capabilities of UAVs in complex environments. A novel UAV equipped with spectral sensing and conducted extensive experiments is developed on a virtual simulation platform. Simulation results demonstrate that the proposed method demonstrating increased robustness and a higher success rate in obstacle avoidance.
Toward Embodied Intelligence: An Autonomous Exploration System for Multimodal UAV
2024-07-05
2239467 byte
Conference paper
Electronic Resource
English