Smart Forest Navigation System Using LoRa and Dynamic Pathfinding

Navigating dense forest environments presents significant challenges due to obstructed GPS signals, dynamic terrain, and limited communication infrastructure. This research proposes a Smart Forest Navigation System that enables real-time, infrastructure-free navigation by integrating LoRa communication, radar-based obstacle detection, and Dynamic A ${}^{\ast }$ (D ${}^{\ast } $ ) pathfinding. The system leverages Time of Arrival (ToA) measurements from LoRa nodes for long-range localization, enhanced through sensor fusion with Inertial Measurement Unit (IMU) data using an Extended Kalman Filter (EKF) on the user’s smartphone. A radar sensor provides real-time environmental perception, detecting obstacles and updating a dynamically evolving map enriched with satellite, drone, and crowdsourced data via probabilistic modeling. Path optimization is handled by the D ${}^{\ast }$ Lite algorithm, which adapts to terrain changes and obstacle updates, using a cost function that accounts for terrain difficulty and energy efficiency. All computations are performed on-device, ensuring low-latency operation without reliance on GPS or external servers. The proposed system offers a cost-effective, energy-efficient solution suitable for forest exploration, wildlife tracking, and search and rescue missions in GPS-denied environments.