Safe and Efficient Exploration Path Planning for Unmanned Aerial Vehicle in Forest Environments

This study presents an enhanced exploration path planning for unmanned aerial vehicles. The primary goal is to increase the chances of survival of missing people in forest environments. Exploration path planning is an essential methodology for exploring unknown three-dimensional spaces. However, previous studies have mainly focused on underground environments, not forest environments. The existing path planning methods for underground environments are not directly applicable to forest environments. The reason is that multiple open spaces exist with various obstacles, such as trees, foliage, undergrowth, and rocks. This study mainly focused on improving the safety and efficiency to be suitable for forests rather than underground environments. Paths closer to obstacles are penalized to enhance safety, encouraging exploration at a safer distance from obstacles. A potential field function is applied based on explored space to minimize overlapping between existing and new paths to increase efficiency. The proposed exploration path planning method was validated through an extensive simulation analysis and comparison with state-of-the-art sampling-based path planning. Finally, a flight experiment was conducted to verify further the feasibility of the proposed method using onboard real hardware implementation in a cluttered and complex forest environment.