  Autonomous inspection of large geographical areas is a central requirement
for efficient hazard detection and disaster management in future cyber-physical
systems such as smart cities. In this regard, exploiting unmanned aerial
vehicle (UAV) swarms is a promising solution to inspect vast areas efficiently
and 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 UAVs
cannot 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 requires
energy-efficient path planning that takes into account the energy constraint of
each individual UAV. In this paper, a novel path planning algorithm is proposed
for performing energy-efficient inspection, under stringent energy availability
constraints for each UAV. The developed framework takes into account all
aspects of energy consumption for a UAV swarm during the inspection operations,
including energy required for flying, hovering, and data transmission. It is
shown that the proposed algorithm can address the path planning problem
efficiently in polynomial time. Simulation results show that the proposed
algorithm can yield substantial performance gains in terms of minimizing the
overall inspection time and energy. Moreover, the results provide guidelines to
determine parameters such as the number of required UAVs and amount of energy,
while designing an autonomous inspection system.
