Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw

Abstract

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.

A Novel Direct Trajectory Planning Approach Based on Generative Adversarial Networks and Rapidly-Exploring Random Tree

“…In response to the above problems, the control-space sampling RRT algorithm is proposed. The control-space sampling RRT samples the feasible nodes by discretizing the control action and action duration [24] to ensure that the planned trajectory is highly feasible. Since the position and orientation guided by the control action need to be calculated by the vehicle analytical model, it is a time-consuming process to ensure that the planned trajectory extends to the target accurately.…”