A UAV with a variable sweep wing has the potential to perform a perched landing on the ground by achieving high pitch rates to take advantage of dynamic stall. This study focuses on the generation and evaluation of a trajectory to perform a perched landing on the ground using a non-linear constraint optimiser (Interior Point OPTimizer) and a Deep Q-Network (DQN). The trajectory is generated using a numerical model that characterises the dynamics of a UAV with a variable sweep wing which was developed through wind tunnel testing. The trajectories generated by a DQN have been compared with those produced by non-linear constraint optimisation in simulation and flown on the UAV to evaluate performance. The results show that a DQN generates trajectories with a lower cost function and have the potential to generate trajectories from a range of starting conditions (on average generating a trajectory takes 174 milliseconds). The trajectories generated performed a rapid pitch up before the landing site is reached, to reduce the airspeed (on average less than 0.5m/s just above the landing site) without generating an increase in altitude, and then the nose dropped just before hitting the ground to allow the aircraft to be recovered without damaging the tail. The trajectories generated by a DQN produced a final airspeed (when it hit the ground)

A variable sweep wing UAV is developed utilising off the shelf components with a custom mechanism for the wing box. The movement of the wing sweep in flight enables large pitching moments suitable for performing perching manoeuvres. Wind tunnel data is presented that confirms the favourable characteristics expected from sweeping the wing and achieving high pitch rates. Whilst only small sweep changes are required during flight, the design allows up to 30 • forward sweep for significant pitching moments during the flare. A new collection of controllers is developed based on observations from similar landing techniques performed by birds and hang-gliders onto flat ground. The three-stage landing process takes the aircraft along an approach path, through a roundout procedure during which airspeed decays and concludes with rapid pitch up. Flight test results are presented during which it is found that the airspeed can be reduced to, on average, under 3m/s in the final moments before landing-well below the stall speed of 9m/s.

This paper presents an evaluation of the benefits of multiobjective optimisation algorithms, compared to single objective optimisation algorithms, when applied to the problem of planning a route over an unstructured environment, where a route has a number of objectives defined using real-world data sources.The paper firstly introduces the problem of planning a route over an unstructured environment (one where no predetermined set of possible routes exists) and identifies the data sources, Digital Terrain Elevation Data (DTED) and NASA Landsat Hyperspectral data, used to calculate the route objectives (time taken, exposure and fuel consumed). A number of different route planning problems are then used to compare the performance of two single-objective optimisation algorithms and a range of multi-objective optimisation algorithms selected from the literature.The experimental results show that the multi-objective optimisation algorithms result in significantly better routes than the single-objective optimisation algorithms and have the advantage of returning a set of routes that represent the trade-off between objectives. The MOEA/D and SMPSO algorithms are shown, in these experiments, to outperform the other multi-objective optimisation algorithms for this type of problem. Future work will focus on how these algorithms can be integrated into a route planning tool and especially on reducing the time taken to produce routes.

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