This paper addresses a methodology for autonomous motion planning of multirotor aircraft in obstructed environments. The control strategy allows the vehicle to online generate quasi-optimal trajectories with limited computational load while performing collision avoidance tasks. The problem is formulated in a model-predictive control architecture in which motion planning and trajectory tracking processes are solved separately. The first process is based on a spline path planning approach to generate smooth and safe trajectories. The second process elaborates trajectory inputs in terms of commanded thrust magnitude and desired attitude rates in order to steer the vehicle during the mission task. Results of both numerical simulations and, for the first time, an experimental validation are provided in order to assess the performance of the approach in the presence of external disturbances and unmodeled dynamics, provided adequate time horizon and update frequency are selected for the numerical optimization algorithm.

Optimal autonomous multirotor motion planning in an obstructed environment

de Angelis E. L.
;
Giulietti F.;Rossetti G.;
2019

Abstract

This paper addresses a methodology for autonomous motion planning of multirotor aircraft in obstructed environments. The control strategy allows the vehicle to online generate quasi-optimal trajectories with limited computational load while performing collision avoidance tasks. The problem is formulated in a model-predictive control architecture in which motion planning and trajectory tracking processes are solved separately. The first process is based on a spline path planning approach to generate smooth and safe trajectories. The second process elaborates trajectory inputs in terms of commanded thrust magnitude and desired attitude rates in order to steer the vehicle during the mission task. Results of both numerical simulations and, for the first time, an experimental validation are provided in order to assess the performance of the approach in the presence of external disturbances and unmodeled dynamics, provided adequate time horizon and update frequency are selected for the numerical optimization algorithm.
de Angelis E.L.; Giulietti F.; Pipeleers G.; Rossetti G.; Van Parys R.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/732324
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