Probability Fuzzy Logic (PFL)
Sameer A. Agrawal, B. K. Patle, Sudarshan B. Sanap · 2025
Unmanned Aerial Vehicles (UAVs) are becoming critical in many applications, including environmental monitoring, search and rescue missions, and delivery services, where efficient and trustworthy navigation is vital. This chapter proposes an innovative application of Probability Fuzzy Logic (PFL) for motion planning of UAVs, addressing both single and multiple UAV scenarios in complex static environments. By combining fuzzy logic with probabilistic models, the PFL technique effectively handles uncertainties and optimizes navigation routes. Simulations conducted using MATLAB demonstrate that UAVs equipped with PFL can efficiently plan and execute paths while statically avoiding obstacles. In scenarios involving a single UAV, the PFL algorithm ensures smooth and collision-free navigation. In multiple UAV scenarios, UAVs independently and cooperatively optimize their routes to avoid conflicts and improve overall navigation efficiency. Significantly, the planned path lengths in both scenarios were consistently within an excess of 20% of the shortest possible Euclidean distances, which highlights the algorithm’s efficiency. These findings validate the robustness and scalability of the PFL approach, showcasing its strong potential for real-world UAV applications in complex environments.