Collaborative mission planning for UAV cluster to optimize relay distance

Unmanned Aerial Vehicles (UAVs) coordinated path planning and intercommunication for visual exploration of a geographical region has recently become crucial. Multiple UAVs cover larger area than a single UAV and eliminate blind spots. To improve the surveillance, survivability and quality of the communication, we propose two algorithms for the route planning of UAV cluster operated in obstacle rich environment: (i) Multiple Population Genetic Algorithm (MPGA) (ii) Relay Selection Criteria (RSC). The main objective of MPGA is to minimize the total mission time while maintaining an optimal distance for communication between the neighboring nodes. MPGA utilizes evolutionary speciation techniques with a novel Feasible Population Creation Method (FPCM) and enhanced Inter-species Crossover Mechanism (ISCM) to obtain diversified routes in remarkably short time. In obtaining collision-free optimum paths, UAVs are subjected to constraints such as limited communication range, maximum maneuverability and fuel capacity. In addition to the path planning, RSC is developed for selection of UAVs relay nodes that is based on the location of the relay relative to source and destination. It is crucial since the Bit Error Rate (BER) performance of the link significantly depends on the location of the selected relay. In this paper, path planning and relay allocation algorithms are combined to have a seamless high quality monitoring of the region and to provide superior Quality of Service (QoS) for audio-video applications. Also, simulations in different operation zones with a cluster of up to six UAVs are performed to verify the feasibility of the proposed algorithms both in optimality and computation time.