We address the problem of providing suitable reference trajectories in motion planning problems for automatically driven vehicles. Among the various approaches to compute a reference trajectory, our aim is to find trajectories which optimize a given performance criterion, for instance fuel consumption, comfort, safety, time, and obey constraints, e.g. collision avoidance, safety regions, control bounds. This task can be approached by optimal control problems, which need to be solved efficiently in real-time. To this end we use direct discretization schemes and nonlinear model-predictive control in combination with sensitivity updates to predict optimal solutions in the presence of perturbations.
Current research topics:
- automatic path planning and online model-predictive control
- automatic obstacle avoidance
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