  This paper presents a novel hierarchical motion planning approach based on
Rapidly-Exploring Random Trees (RRT) for global planning and Model Predictive
Control (MPC) for local planning. The approach targets a three-wheeled cycle
rickshaw (trishaw) used for autonomous urban transportation in shared spaces.
Due to the nature of the vehicle, the algorithms had to be adapted in order to
adhere to non-holonomic kinematic constraints using the Kinematic Single-Track
Model.
  The vehicle is designed to offer transportation for people and goods in
shared environments such as roads, sidewalks, bicycle lanes but also open
spaces that are often occupied by other traffic participants. Therefore, the
algorithm presented in this paper needs to anticipate and avoid dynamic
obstacles, such as pedestrians or bicycles, but also be fast enough in order to
work in real-time so that it can adapt to changes in the environment. Our
approach uses an RRT variant for global planning that has been modified for
single-track kinematics and improved by exploiting dead-end nodes. This allows
us to compute global paths in unstructured environments very fast. In a second
step, our MPC-based local planner makes use of the global path to compute the
vehicle's trajectory while incorporating dynamic obstacles such as pedestrians
and other road users.
  Our approach has shown to work both in simulation as well as first real-life
tests and can be easily extended for more sophisticated behaviors.
