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In diesem Beitrag wird eine Pfadfolgeregelung für Lastenpedelecs, die autonom ein- und ausparken sollen, untersucht. Es wird zunächst ein Modell zur Beschreibung der Bewegung von Lastenpedelecs hergeleitet. Danach wird ein für den Anwendungsfall passendes Verfahren zur Pfadfolgeregelung ausgewählt und modelliert. Als Letztes wird der Pfadfolgeregler in MATLAB/Simulink zusammen mit dem Modell des Lastenpedelecs implementiert und verschiedene Testszenarien untersucht. / In this paper, a path following control for cargo pedelecs is investigated, which shall be autonomously parking. First, a model for the description of the movement of cargo pedelecs is derived. Then, a path following control method suitable for the use case is selected and modeled. Finally, the path following controller is implemented in MATLAB/Simulink together with the model of the cargo pedelec and various test scenarios are investigated.

Path planning that considers accurate vehicle dynamics and obstacle avoidance is an important problem in the area of autonomous driving. This paper describes a method of implementing trajectory planning for autonomous parking in conditions where the starting point and the position of fixed obstacles are uncertain. The narrow spaces and complicated manoeuvres required for parking demands a lot from the trajectory planning algorithm. It needs to have the ability to accurately model vehicle dynamics and find an efficient way around obstacles. Having obstacles in the way of the parking vehicle makes this a nonconvex problem the goal can usually not be reached by travelling in a straight line and finding a perfect trajectory around them is generally not computationally tractable. This paper reviews a two tiered approach to solving this problem. First a rough path is found using a modified Rapidly-exploring Random Tree (RRT) algorithm called Forward-Backward RRT, which runs two treebuilding processes in parallel and constructs a feasible path from where they intersect. Using optimisation this is then improved into a trajectory that is at least a local optimum. These methods will be demonstrated to produce efficient and feasible trajectories that respects the dynamic constraints of the vehicle and avoids collisions.

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