FAEP: Fast Autonomous Exploration Planner for UAV Equipped with Limited FOV Sensor
This work addresses the need for faster and more efficient autonomous exploration in UAV operations, representing an incremental improvement over existing methods.
The paper tackles the problem of autonomous exploration for UAVs with limited field-of-view sensors by proposing FAEP, a planner that improves exploration efficiency through novel path and heading strategies, achieving superior performance in benchmarks and real-world tests compared to typical and state-of-the-art methods.
Autonomous exploration is one of the important parts to achieve the autonomous operation of Unmanned Aerial Vehicles (UAVs). To improve the efficiency of the exploration process, a fast and autonomous exploration planner (FAEP) is proposed in this paper. We firstly design a novel frontiers exploration sequence generation method to obtain a more reasonable exploration path, which considers not only the flight-level but frontier-level factors into TSP. According to the exploration sequence and the distribution of frontiers, a two-stage heading planning strategy is proposed to cover more frontiers by heading change during an exploration journey. To improve the stability of path searching, a guided kinodynamic path searching based on a guiding path is devised. In addition, a dynamic start point selection method for replanning is also adopted to increase the fluency of flight. We present sufficient benchmark and real-world experiments. Experimental results show the superiority of the proposed exploration planner compared with typical and state-of-the-art methods.