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Autonomous inspection of large geographical areas is a central requirementfor efficient hazard detection and disaster management in future cyber-physicalsystems such as smart cities. In this regard, exploiting unmanned aerialvehicle (UAV) swarms is a promising solution to inspect vast areas efficientlyand with low cost. In fact, UAVs can easily fly and reach inspection points,record surveillance data, and send this information to a wireless base station(BS). Nonetheless, in many cases, such as operations at remote areas, the UAVscannot be guided directly by the BS in real-time to find their path. Moreover,another key challenge of inspection by UAVs is the limited battery capacity.Thus, realizing the vision of autonomous inspection via UAVs requiresenergy-efficient path planning that takes into account the energy constraint ofeach individual UAV. In this paper, a novel path planning algorithm is proposedfor performing energy-efficient inspection, under stringent energy availabilityconstraints for each UAV. The developed framework takes into account allaspects of energy consumption for a UAV swarm during the inspection operations,including energy required for flying, hovering, and data transmission. It isshown that the proposed algorithm can address the path planning problemefficiently in polynomial time. Simulation results show that the proposedalgorithm can yield substantial performance gains in terms of minimizing theoverall inspection time and energy. Moreover, the results provide guidelines todetermine parameters such as the number of required UAVs and amount of energy,while designing an autonomous inspection system.

Optimized Path Planning for Inspection by Unmanned Aerial Vehicles Swarm with Energy Constraints