Online optimization and trajectory planning are key aspects of autonomous deep space missions. Taking into account individual target criteria, such as time or energy optimality, any spacecraft maneuver can be traced back to a general problem definition of the form "move the spacecraft from its initial state to a desired final state, while considering a dynamic model and avoiding collisions". This corresponds to an optimal control problem (OCP) and can be solved using WORHP, the ESA solver for non-linear programming (NLP). More specifically, the OCP is transcribed into an NLP by time discretization and the corresponding optimal trajectory - including control variables - is calculated by sequential quadratic programming. In order to obtain a highly efficient solution algorithm, the naturally occurring sparsity of the Jacobian and the Hessian is exploited.
The effectiveness of this approach has already been demonstrated in several DLR projects, such as the deep space missions KaNaRiA and EnEx-CAUSE. In order to make such results immediately available for terrestrial applications, a transfer to current scientific questions is appropriate. Moreover, the transfer would provide a test platform and increase public acceptance. Conversely, the knowledge gained from terrestrial testing can help planning more detailed space missions.
In this work, the DLR project AO-Car for controlling an autonomous vehicle in road traffic is presented as such a transfer. The concept, originally developed in the context of KaNaRiA for trajectory planning and control, is successfully implemented on a research vehicle, a VW Passat. The vehicle is able to explore a parking area autonomously, to identify free parking spaces and to perform a parking maneuver. During the exploration, suddenly appearing objects are recognized. Depending on the scene, a collision avoidance trajectory is computed or an emergency stop is performed. The method presented is based on WORHP and offers a uniform framework for optimal driving maneuvers. It is highly flexible, as reaction speed and passenger comfort can be easily balanced by adaptive weighting of target criteria.