Positioning of UAV using algorithm for monitering the forest region

Development in the area of autonomous motion planning has enabled the utilization of fully autonomous Unmanned Aerial Vehicles (UAV). This would enable unmanned vehicles to take shorter paths and avoid collisions in obstacle rich environment. The approach is analysed on a sampling based algorithm, rapidly-exploring PYTHON software. This article proposes an algorithm called ‘Haversine theory’ building on the basic algorithm, providing an alternative to probabilistic location, thereby avoiding local collision. The approach is a benchmarking for kinematic car, dynamic car and a quadrotor and the results show improvements in length of the motion plans and the time of computing.