Strategic control to enhance the safety of unmanned aerial vehicles using artificial intelligence

all these path planning algorithms need to model the environment in advance, which is not suitable for the problem of global path pre-planning of drones in complex environment. Unmanned Aerial Vehicles (UAVs) are widely deployed in military surveillance operations, especially the quadcopter UAVs which are easy to operate and considerably quieter. However, UAVs encounter problems in secure path planning during navigation and are prone to cyber security attacks. Further, due to the UAV battery capacity, the operating time for surveillance is limited. In this paper, we propose a novel Resilient UAV Path Optimization Algorithm which provides an optimal path under security attacks such as denial-of-service attacks. The performance efficiency of the proposed path planning algorithm is compared with the existing path planning algorithms based on execution time. To achieve secure path planning in UAVs and to mitigate security attacks, a blockchain-aided security solution is proposed. To prevent security attacks, smart contracts are generated where the devices are registered with gasLimit.

Strategic control to enhance the safety of unmanned aerial vehicles using artificial intelligence Keywords:

UAV , artificial intelligence , algorithm , surveillance