Motion Planner for Autonomous Articulated Trucks using Input Sampling Approach

Abstract: In the near future, cars are expected to be able to drive themselves independently and navigate in urban and rural environments. This master thesis concerns the motion planning problem for an autonomous articulated heavy-duty trucks. The objective of this work is to investigate the feasibility of using input sampling approach for heavy-duty trucks in a highway environment. This includes developing the algorithm by taking into account the vehicle’s dimensions and constraints. The developed algorithm is designed to consider static obstacles in a multi-lanes highway road to find a feasible and collision-free path from the current truck’s location to the desired location. The collision avoidance is done by performing drivable region check. The resulting planner successfully performs complex maneuvers such as lane following, lane change, and double lane change. The experiments show that the planner’s execution time is related to the vehicle model, number of samples, and number of lanes being used in the simulation. In the highway environment, the experiments show that the execution time increases exponentially with respect to the number of samples and number of lanes. Further, the vehicle model has a significant impact on the execution time. By adding a trailer to the vehicle model the execution time is increased by approximately 54%.