EFP: Efficient Frontier-Based Autonomous UAV Exploration Strategy for Unknown Environments

The optimization of quadrotors for the efficient and autonomous exploration of complex, unknown environments and the construction of corresponding maps with integrity is of high priority in unmanned aerial vehicle(UAV) research. To overcome the challenges of inefficient and incomplete map construction in autonomous UAV exploration, this study propose EFP, an efficient frontier-based autonomous UAV exploration strategy for unknown environments. For this, the UFOMap algorithm was adopted to represent an entire environment and reduce the map construction time. Its accurate representation and hierarchical frontiers structure were then employed to rapidly extract frontiers. Simultaneously, a fast Euclidean clustering approach was implemented to process the frontiers and obtain the relevant viewpoints, an approximate trajectory optimization strategy was used to rapidly obtain a preferred trajectory that traverses all the viewpoints, and finally the RRT-based global planner and sampling-based local planner algorithms were utilized to perform autonomous exploration with a drone. The proposed algorithm was analyzed and validated in both simulation and real-world scenarios, demonstrating higher efficiency than state-of-the-art approaches and enabling quadrotors to autonomously explore and construct complete maps in complex and unknown environments.