Efficient UAV Exploration with Hybrid Global–Local Strategy and Adaptive Yaw Planning

Abstract

Autonomous exploration in complex environments is frequently hindered by inefficient back-and-forth movements and repetitive revisits to previously explored areas. To address these drawbacks, we propose a two-mode hybrid dynamic exploration strategy that detects isolated frontier clusters and adaptively switches between two modes: global exploration mode (GEM) and local clearance mode (LCM). The GEM generates sequences for frontier exploration access, while the LCM employs a flight-time greedy approach to select and clear isolated clusters, thereby avoiding redundant visits. In addition, to achieve adaptive yaw planning, the proposed exploration strategy generates a reference yaw sequence based on the frontiers near the path trajectory. The reference yaw sequence is then used to perform yaw optimization, with non-uniform B-spline time adjustments ensuring feasible yaw trajectories, fully leveraging the UAV's maneuverability and perception capabilities, and providing a plug-and-play solution for exploration research. Extensive simulations compared to state-of-the-art methods demonstrate that our approach significantly reduces both exploration time and distance, with real-world experiment confirming its practical effectiveness.