On the fuel and energy consumption optimization problem in aircraft path planning

L’Afflitto, Andrea; Sultan, Cornel

doi:10.1109/cdc.2010.5717598

Cited by 2 publications

(1 citation statement)

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“…Other methods have relied on developing analytical models to directly calculate the fuel consumption instead of relying on experimental datasets. This is the case of [ 5 ], in which L’Afflitto and Sultan modeled the aircraft as a six degrees-of-freedom rigid body. However, their method made several assumptions and did not consider the altitude of the aircraft or the propeller efficiency.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization

Roberge,

Labonté,

Tarbouchi

2024

Sensors

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This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton’s equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU.

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Section: Introductionmentioning

confidence: 99%