Trajectory Estimation And Motion Planning In Unmanned Aerial Vehicle
There is always inquisitiveness in people about flying objects or aircrafts. It is a fascinating thing for an individual
to build such a model that can truly fly in air and can be controlled. “Quadrotor” is one of the appealing and interesting
designs of a flying object. As per the studies advancements in manual controlled Quadrotor have reached to ultimate extent.
Autonomous Quadrotor still has much scope of enhancement. Autonomous Quadrotor needs Trajectory to be defined that is
the path to be followed by the Quadrotor. Reinforcement learning strategies can strengthen the power of Quadrotor by
progressively improving the trajectory estimation and motion planning. This requires processors with high computational
efficiency to process camera captured data and redefine path and motions at every point on trajectory. In this paper we
present methodology to optimize processing and reduce computational overheads implementing reinforcement leaning by
which continuous monitoring of Quadrotor can be avoided. This will help to explore the potentials of a Quadrotor and can
have various real time applications.