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Optimal stochastic Filtering and Path Generation for Race Cars in a competitive Environment


Michael Janser

Master Thesis, HS11 (10194)

The Optimal Autonomous RC Racer (ORCA Racer) project studies high-speed, realtime control of 1:43 scale race cars. In this thesis, which is also part of the ORCA project, a controller is derived, which is able to sustain in a competitive environment such as a car race game. Besides the ability of on-line trajectory optimization the derived controller shows also responsiveness to events such as obstacles on the track and has the ability of avoiding a crash. Additionally basic game strategies, which enable the controller to overtake slower opponents and try to block faster ones, were implemented. For this purpose, a model predictive contouring control (MPCC) controller was used. A high level controller was implemented above the low level MPCC for being able to overtake opponents and react on events occurring in a race. The high level controller is responsible for dynamically changing the state constraints of the MPCC and adapting them to the new conditions, in order to avoid a crash during overtaking manoeuvres. This is realized by minimize a cost function and synthesizing a path using dynamic programming. The newly derived high level controller was tested in simulation using di erent scenarios. It could be shown, that this method is a promising approach for on-line path planning and obstacle avoidance in a comparative environment. With an incorporated IMM lter, which was responsible for predicting the trajectory of the opposing cars and was also developed at the ORCA project, the controller could successfully nish 84% of the overtaking manoeuvres in the simulation environment.


Type of Publication:

(12)Diploma/Master Thesis

S. Summers

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2013:IFA_4334
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