  While using drones for remote surveillance missions, it is mandatory to do
path planning of the vehicle since these are pilot-less vehicles. Path
planning, whether offline or online, entails setting up the path as a sequence
of locations in the 3D Euclidean space, whose coordinates happen to be
latitude, longitude and altitude. For the specific application of remote
surveillance of long linear infrastructures in non-urban terrain, the
continuous 3D-ESP problem practically entails two important scalar costs. The
first scalar cost is the distance traveled along the planned path. Since drones
are battery operated, hence it is needed that the path length between fixed
start and goal locations of a mission should be minimal at all costs. The other
scalar cost is the cost of transmitting the acquired video during the mission
of remote surveillance, via a camera mounted in the drone's belly. Because of
the length of surveillance target which is long linear infrastructure, the
amount of video generated is very high and cannot be generally stored in its
entirety, on board. If the connectivity is poor along certain segments of a
naive path, to boost video transmission rate, the transmission power of the
signal is kept high, which in turn dissipates more battery energy. Hence a path
is desired that simultaneously also betters what is known as communication
cost. These two costs trade-off, and hence Pareto optimization is needed for
this 3D bi-objective Euclidean shortest path problem. In this report, we study
the mono-objective offline path planning problem, based on the distance cost,
while posing the communication cost as an upper-bounded constraint. The
bi-objective path planning solution is sketched out towards the end.
