Motion- and Communication-Planning of Unmanned Aerial Vehicles in Delay Tolerant Network using Mixed-Integer Linear Programming

Abstract

Large amounts of data are typically generated in applications such as surveillance of power lines and railways, inspection of gas pipes, and security surveillance. In the latter application it is a necessity that the data is transmitted to the control centre "on-the-fly" for analysis. Also missions related to other applications would greatly benefit from near real-time analysis and operator interaction based on captured data. This is the motivation behind this paper on coarse offline motion-and communication-planning for cooperating Unmanned Aerial Vehicles (UAVs). A Mixed-Integer Linear Programming (MILP) problem is defined in order to solve the surveillance mission. To efficiently transmit the data back to the base station the vehicles are allowed to store data for later transmission and transmit via other vehicles, in addition to direct transmission.The paths obtained by solving the optimization problem are analyzed using a realistic radio propagation path loss simulator. If the radio propagation path loss exceeds the maximum design criterion the optimization problem is solved again with a stricter communication constraint, and the procedure is continued in an iterative manner until the criterion is met. The proposed algorithm is supported by simulations showing the resulting paths and communication topologies for different choices of delay tolerance.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

“…Therefore, drone positioning as a flying base station depends on signal fading along the communication path, path loss, interferences, and noise. Other connectivity challenges emerge when several drones perform tasks cooperatively, since they may need to exchange information by establishing communication links that are subject to noise and dependent on transmission distance . Drone‐to‐drone communication also enables drones to attend a GPS‐denied area while maintaining a communication link with another drone able to receive the GPS signal .…”

“…Overall, because data collection and transmission consume the limited energy of sensors and drones, a widely used objective function is to maximize the WSN lifetime, that is, the time interval before the first sensor failure due to energy expiration , or maximal energy consumption among sensors to transmit the collected information . Some articles minimize makespan , total travel distance of the drone and the energy required for the drone's operations .…”

“…First, since the speed of direct data transmission between two nodes is usually faster than the flying speed of a drone, multihop transmissions to the base station may increase the recency of the received information from drones that are out of the direct communication range. It may also free up the limited buffer size of the drone for further data collection . Second, communication between drones is important for flying in formation .…”

Survey on Communication and Networks for Autonomous Marine Systems

“…Using mechanisms such as delay-tolerant networking, the payload data can be physically transported by the vehicle into positions where communication performance is sufficient [88]. Using path loss simulation software and optimisation techniques, the overall mission can be optimised given constraints on latencies and other mission parameters [89]. When optimising the mobility of network nodes, the communication network topology may also be autonomously and dynamically reconfigured, given the requirements of the different nodes at the time.…”

Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming

“…MILP problems have been proposed for unmanned aerial vehicles by Schouwenaars et al (2001), , and . After these initial works, several other authors have used MILP models for a) unmanned aerial vehicles (Schouwenaars et al, 2004b;Borrelli et al, 2006;Schouwenaars et al, 2006;Vitus et al, 2008;Kuwata and How, 2011;Richards and Turnbull, 2015;Turnbull et al, 2016;Grøtli and Johansen, 2016;Radmanesh and Kumar, 2016;Richards and How, 2006); and b) wheeled robots (Earl and D'Andrea, 2005).…”