ECONOMY Point Clouds-based Energy-efficient Autonomous Navigation for UAVs

Abstract : Most UAVs depend on realtime 3D point clouds to navigate through unknown environments. However, both point clouds processing and trajectory planning are computationally expensive and will deplete UAV's battery quickly. There are also inevitable uncertainties in point clouds, which further makes collision-free trajectory planning a very challenging problem. To address these issues, we propose an energy-efficient and cloud-assisted autonomous navigation system, called ECONOMY, which allows an UAV to transmit the point clouds through a cellular network to a computing cloud that plans the trajectories for the UAV in realtime. To maximize the UAV's energy-efficiency, we jointly optimize its velocity, transmission and reception power. Since the formulated problem is non-convex, we decompose it into a UAV velocity and trajectory optimization problem at a specific time t, and a UAV communication optimization problem at t, which are, in turn, resolved by an intelligent solver based on the Suggest-and-Improve framework, and coordinate gradient ascend. To address point clouds uncertainties, we devise probabilistic collision-free constraints which can be handled in a deterministic manner by exploiting the tight (N,2,R^N)-upper bound for convex sets. Simulation results on an UAV exploring a simulated urban environment demonstrate the efficacy of ECONOMY and its superiority over baseline systems.

 EXISTING SYSTEM :

 ? Then a scan matching algorithm is implemented for aligning the laser scans and with the existing map to estimate the UAV states (2D position and heading). ? We argue that such mobile sensing/actuation platforms have already matured to the point where they are widely considered as a viable addition to existing application and approaches. ? These advances will make it possible to use UAVs as flexible and mobile platforms (potentially operating in swarms) and to integrate them into the smart city infrastructure. ? The market for UAVs is expanding rapidly as new applications are emerging, with new models and suppliers entering the market frequently.

 DISADVANTAGE :

 ? Moreover, remote measurement and control technologies for UAVs present certain problems. ? Eventually, as UAVs are more frequently used in modern airspace, flight monitoring and collision avoidance systems will face issues of scale. ? Issues regarding various protocols for long- and short-range wireless communication between UAV systems and UAV control entities have been well researched, but solutions for a scalable monitoring system are lacking. ? There are various issues to address in order to realize the effective, stable and reliable use of UAVs, i.e., network topology, routing, seamless handover, energy efficiency and management.

 PROPOSED SYSTEM :

 • The proposed algorithm, that allows the quad-copter to navigate autonomously in a GPS denied environment, is simulated using the MATLAB and the simulation results are presented. • The procedure to accomplish the flight simulation is to first have the vertical take-off from an initial point, and then calculate the subsequent way-points via the proposed algorithm. • Unmanned aerial vehicles (UAVs) are used for a variety of purposes including remote sensing, firefighting, search and rescue (SAR) operations, monitoring, and surveillance. • As proposed in, UAVs can be used to locate civil security units such as fire fighters or policemen if they are equipped with transponders.

 ADVANTAGE :

 ? We develop our proposed system and demonstrate its feasibility and performances through simulation. ? This remote measurement platform increases the accuracy of control and management, but fails to consider collision avoidance and provides no mention of performance when connecting a large number of UAVs. ? We implement UAV Flight Tracker and evaluate the performance in terms of response time, storage/memory overhead and collision avoidance. ? To express the performance and scalability of UAV Flight Tracker adequately, the latency of data sent between the client, server and UAV must be recorded along with the size of data being held on the server. ? The number of concurrently active UAVs is the most significant factor in determining server response time and client performance.