A Non-linear MPC Local Planner for Tractor-Trailer Vehicles in Forward and Backward Maneuvering

TL;DR

This paper introduces a novel non-linear MPC local planner for tractor-trailer vehicles that effectively manages forward and backward maneuvers, obstacle avoidance, and stability in autonomous driving scenarios.

Contribution

A practical NMPC local planner is developed for tractor-trailers, capable of handling jackknife, obstacle avoidance, and path following in both maneuvering directions.

Findings

Achieves stable obstacle avoidance in simulations

Provides accurate path tracking in real-time scenarios

Successfully integrated with AutowareAi software stack

Abstract

Designing a local planner to control tractor-trailer vehicles in forward and backward maneuvering is a challenging control problem in the research community of autonomous driving systems. Considering a critical situation in the stability of tractor-trailer systems, a practical and novel approach is presented to design a non-linear MPC(NMPC) local planner for tractor-trailer autonomous vehicles in both forward and backward maneuvering. The tractor velocity and steering angle are considered to be control variables. The proposed NMPC local planner is designed to handle jackknife situations, avoiding multiple static obstacles, and path following in both forward and backward maneuvering. The challenges mentioned above are converted into a constrained problem that can be handled simultaneously by the proposed NMPC local planner. The direct multiple shooting approach is used to convert the optimal control problem(OCP) into a non-linear programming problem(NLP) that IPOPT solvers can solve in CasADi. The controller performance is evaluated through different backup and forward maneuvering scenarios in the Gazebo simulation environment in real-time. It achieves asymptotic stability in avoiding static obstacles and accurate tracking performance while respecting path constraints. Finally, the proposed NMPC local planner is integrated with an open-source autonomous driving software stack called AutowareAi.