The Time Dependent Traveling Salesman Planning Problem in Controlled Airspace

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

“…In contrast to trucks, however, drones may select a battery of the most suitable size for the tour, taking into account that energy consumption depends heavily on the weight of the drone . Drones also fly in 3D space without a road network and may have to make a detour to avoid obstacles or dangerous zones (eg, bad weather regions) .…”

“…Persiani and Bagassi and Furini et al assume shared airspace. The authors consider path replanning for a drone as the lowest priority participant in air traffic.…”

“…These two studies each formulate a time‐dependent TSP to sequence a drone's visit points, and determine its trajectory in space and time, considering important properties of motion such as possible ascent and descent angles. Furini et al formulate a mixed‐integer program and work out several well‐performing cuts for this problem.…”

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 ...…”

Stochastic Drone Fleet Deployment and Planning Problem Considering Multiple-Type Delivery Service

“…In the drone delivery systems of JD.com and Ele.me, (drone) service routes are designated and fixed and the optimization of drone fleet deployment and planning (DFDP) decisions can greatly improve the delivery efficiency. On each service route, to ensure a safe and ordered flow of the air traffic in the controlled airspace, two consecutive drones departing from the warehouse to deliver parcels must respect a certain time interval (Furini et al [14]). According to Amazon's drone delivery scheme, the drone operating cost mainly includes the amortized purchasing cost and the maintenance cost, which depend largely on the drone type and number (Keeney [15]).…”