A path planning and path-following control framework for a general 2-trailer with a car-like tractor

Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabilize the lateral and angular path-following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed which only utilizes information from sensors that are mounted on the car-like tractor, making the system independent of additional trailer sensors. The proposed path planning and path-following control framework is implemented on a full-scale test vehicle and results from simulations and real-world experiments are presented.Comment: Preprin

Safe Trajectory Tracking in Uncertain Environments

In Model Predictive Control (MPC) formulations of trajectory tracking problems, infeasible reference trajectories and a-priori unknown constraints can lead to cumbersome designs, aggressive tracking, and loss of recursive feasibility. This is the case, for example, in trajectory tracking applications for mobile systems in the presence of constraints which are not fully known a-priori. In this paper, we propose a new framework called Model Predictive Flexible trajectory Tracking Control (MPFTC), which relaxes the trajectory tracking requirement. Additionally, we accommodate recursive feasibility in the presence of a-priori unknown constraints, which might render the reference trajectory infeasible. In the proposed framework, constraint satisfaction is guaranteed at all times while the reference trajectory is tracked as good as constraint satisfaction allows, thus simplifying the controller design and reducing possibly aggressive tracking behavior. The proposed framework is illustrated with three numerical examples.Comment: 13 pages, 6 figures, submitted to IEEE Transactions on Automatic Control, code availabl

Evaluation of Local Kinematic Motion Planning Algorithms for a Truck and Trailer System

Over the past few decades, researchers have worked towards developing autonomous systems that can be used in everyday transportation, and with the emergence of new sensor, hardware, and software technologies, the goal of self-driving vehicles is now on the brink of becoming a reality. In order for these systems to properly plan and react to their complex environments, they need to be equipped with the proper tools and algorithms to ensure safe deployment for all stakeholders. Navigating tight spaces with truck and trailer systems in dynamic environments can be a difficult task due to their nonlinear dynamics, delayed actuation, and large physical dimensions. This thesis presents a kinematic approach to local motion planning for truck and trailer vehicles in the forward motion. This approach was applied to the sample-based planning algorithms RRT* and RRTᵡ in order to adapt and replan in the presence of dynamic obstacles. A combined motion planning and control framework was then developed and deployed in both simulations, using American Truck Simulator, and on an International ProStar 122+ truck. After the feedback controllers were iteratively tuned, the motion planners were evaluated alongside a deterministic Hybrid A* approach using a lane change and seaport scenario with simulated static and dynamic obstacles. In both cases, the approach demonstrated the ability for the sample-based planner approach to provide real-time and feasible plans for the controller to execute at low speeds while maintaining a safe distance away from nearby obstacles