UAV Routing and Coordination in Stochastic, Dynamic Environments

Abstract

Recent years have witnessed great advancements in the science and technology for unmanned aerial vehicles (UAVs), e.g., in terms of autonomy, sensing, and networking capabilities. This chapter surveys algorithms on task assignment and scheduling for one or multiple UAVs in a dynamic environment, in which targets arrive at random locations at random times, and remain active until one of the UAVs flies to the target's location and performs an on-site task. The objective is to minimize some measure of the targets' activity, e.g., the average amount of time during which a target remains active. The chapter focuses on a technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. The main advantage of this approach is its ability to provide analytical estimates of the performance of the UAV system on a given problem, thus providing insight into how performance is affected by design and environmental parameters, such as the number of UAVs and the target distribution. In addition, the approach provides provable guarantees on the system's performance with respect to an ideal optimum. To illustrate this approach, a variety of scenarios are considered, ranging from the simplest case where one UAV moves along continuous paths and has unlimited sensing capabilities, to the case where the motion of the UAV is subject to curvature constraints, and finally to the case where the UAV has a finite sensor footprint. Finally, the problem of cooperative routing algorithms for multiple UAVs is considered, within the same queueing-theoretical framework, and with a focus on control decentralization.

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

“…Because service times of such surveillance requests are often uncertain, a variant of the ‐vehicle dynamic traveling repairman problem often arises ; see Bullo et al and Ritzinger et al for surveys. Enright et al , Pavone , Pavone et al and Pavone et al provide several algorithms that result in a bounded expected waiting time for request completion (ie, time from the request arrival to the request completion). Moreover, some algorithms guarantee system stability and have suitable properties both under light‐load conditions, that is, when only a few requests per drone of low required service time arise, and under heavy‐load conditions.…”

UAV Mission Planning Resistant to Weather Uncertainty

“…In general vehicle routing problems, the standard objective function is typically time minimization for visiting a set number of nodes. In UAV fleet mission planning, several variants of objective functions such as reducing individual UAV costs, increasing safety in operations, reducing lead time, and increasing the load capacity of the entire system are considered [3,[21][22][23][24]. Furthermore, the problem can be considered an extension of the vehicle routing and scheduling problems and belongs to the class of NP-hard problems [2,4].…”

The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review

“…Methods and applications for the selected literature Table A.4 highlights the methods and practical applications addressed in the 70 selected papers. -Sabban et al (2012) Markov decision process Path planning in uncertain wind conditions Babel (2011) Shortest path algorithms UAV path planning with obstacles Babel (2012) Shortest path algorithms Path planning in a risk environment Bae et al (2015) Dynamic programming and heuristics Risk-constrained shortest path for UCAV Baiocchi (2014) Heuristic algorithms Path planning for aerial photography Bandeira et al (2015) Heuristic algorithms UAV routing for aerial photography Bednowitz et al (2012) Simulation model UAV routing in dynamic environment Besada-Portas et al (2010) Evolutionary algorithms Real-time UAV path planning Besada-Portas et al 2013Evolutionary algorithms Real-time UAV path planning Casbeer & Holsapple 2011Column generation UAV TA with precedence Chakrabarty & Langelaan (2011) Energy map method Path planning for soaring UAVs Chen et al 2016Genetic algorithm Multi UAV trajectory optimisation Choe et al (2016) Pythagorean hodograph bézier curves Cooperative path planning Cobano et al (2013) Rapid exploring random trees Cooperative trajectory optimisation Cons et al (2014) Heuristic algorithms Integrated TA and path planning Crispin (2016) Rapid exploring random trees Path planning for aerial gliders Dilão & Fonseca (2013) Heuristic algorithms Path planning for a hypersonic glider Edison & Shima (2011) Genetic algorithm Integrated TA and path planning Enright et al (2015) Queueing theory UAV routing in stochastic environments Evers et al (2014) ILS metaheuristic UAV orienteering problem with time windows Faied et al (2010) Mixed-Integer Linear Programming Multi UAV routing problem Filippis et al (2011) Shortest path algorithms UAV path planning with obstacles Forsmo (2012) Mixed-Integer Linear Programming UAV routing and trajectory optimisation Fügenschuh & Müllenstedt (2015) Mixed-Integer Linear Programming UAV routing and trajectory optimisation Furini et al (2016) Mixed-Integer Linear Programming Time dependent UAV routing problem ...…”