7 research outputs found
Sequential Monte Carlo Optimisation for Air Traffic Management
This report shows that significant reduction in fuel use could be achieved by
the adoption of `free flight' type of trajectories in the Terminal Manoeuvring
Area (TMA) of an airport, under the control of an algorithm which optimises the
trajectories of all the aircraft within the TMA simultaneously while
maintaining safe separation. We propose the real-time use of Monte Carlo
optimisation in the framework of Model Predictive Control (MPC) as the
trajectory planning algorithm. Implementation on a Graphical Processor Unit
(GPU) allows the exploitation of the parallelism inherent in Monte Carlo
methods, which results in solution speeds high enough to allow real-time use.
We demonstrate the solution of very complicated scenarios with both arrival and
departure aircraft, in three dimensions, in the presence of a stochastic wind
model and non-convex safe-separation constraints. We evaluate our algorithm on
flight data obtained in the London Gatwick Airport TMA, and show that fuel
saving of about 30% can be obtained. We also demonstrate the flexibility of our
approach by adding noise-reduction objectives to the problem and observing the
resulting modifications to arrival and departure trajectories
Comparison of Branching Strategies for Path-Planning with Avoidance using Nonlinear Branch-and-Bound
Sequential Monte Carlo Optimisation for Air Traffic Management
This report shows that significant reduction in fuel use could be achieved by the adoption of `free flight' type of trajectories in the Terminal Manoeuvring Area (TMA) of an airport, under the control of an algorithm which optimises the trajectories of all the aircraft within the TMA simultaneously while maintaining safe separation. We propose the real-time use of Monte Carlo optimisation in the framework of Model Predictive Control (MPC) as the trajectory planning algorithm. Implementation on a Graphical Processor Unit (GPU) allows the exploitation of the parallelism inherent in Monte Carlo methods, which results in solution speeds high enough to allow real-time use. We demonstrate the solution of very complicated scenarios with both arrival and departure aircraft, in three dimensions, in the presence of a stochastic wind model and non-convex safe-separation constraints. We evaluate our algorithm on flight data obtained in the London Gatwick Airport TMA, and show that fuel saving of about 30% can be obtained. We also demonstrate the flexibility of our approach by adding noise-reduction objectives to the problem and observing the resulting modifications to arrival and departure trajectories