Path-tracking of a tractor-trailer vehicle along rectilinear and circular paths: A Lyapunov-based approach

Published versio

Error adaptive tracking for mobile robots

In mobile robots it is usual that the desired trajectory is memorized or previously generated. When following a trajectory, there are several possibilities attending to the way in which the actual robot state can be related with the whole trajectory. One of them is the extension of the servosystem approach, usually called "trajectory tracking". This is the only possibility if we need strict temporal deterministic requirements. But if not, other possibilities appear. One of them is called "path following", where the path's point to track is the "nearest" (under several conditions) to the actual robot's position. In this paper we present another method suitable for nondeterministic systems, which we may call "error adaptive tracking", because the tracking pace adapts to the errors. Its benefits and advantages are identified. Afterwards, we determine how to construct this method and we apply it to the case of SIRIUS, an advanced wheelchair. Then a control law that ensures asymptotic stability is extracted using the second Lyapunov method and under the error adaptive tracking approach. Finally, we show the benefits of the new method, comparing it with the trajectory tracking approach.Ministerio de Ciencia y Tecnología TIC-2000-0087-P4-

From Active Perception to Active Cooperation Fundamental Processes of Intelligent Behavior

In the ten years since we put forward the idea of active perception (Bajcsy 1985, Bajcsy 1988) we have found that cooperative processes of various kinds and at various levels are often called for. In this paper we suggest that a proper understanding of cooperative processes will lead to a foundation for intelligent behavior and demonstrate the feasibility of this approach for some of the difficult and open problems in the understanding of intelligent behaviors

"Path-tracking for car-like and tractor-trailer-like robots"

Vehicle's dynamic model -- Path tracking -- Controller design -- An application example -- The case of a car-like robot

Guidage assisté par ordinateur d'un tracteur remorque

Cette étude concerne un système de guidage assisté qui permette de simplifier l’exécution de la part d’un conducteur des manœuvres requises par le stationnement en espace restreint d’un tracteur remorque. L’objectif est de valider d’une façon expérimentale le bon fonctionnement du système qui est prévu par la théorie. Cet objectif est poursuivi en mettant en place un banc d’essai approprié et en développant un certain nombre de tests autant expérimentaux que par voie de simulation. Le système de guidage est conçu en appliquant des techniques récemment proposées par la littérature scientifiq ue dans le cadre de l’asservissement de véhicules articulés. Le banc d’essai est centré sur un robot mobile mis au point au sein du Laboratoire d’Automation et Systèmes automatique de l’Ecole Polytechnique de Montréal et sur un environnement informatique pour la commande en temps réel basé sur Matlab, Simulink and XPC target. Les tests concernent, entre autres, l’exécution par voie manuelle et assistée de manoeuvres de stationnement à 90°. Les résultats obtenus permettent d’établir la validation expérim entale recherchée

Stabilisation d'un tracteur-remorque : étude expérimentale de différents contrôleurs

Banc d'essais -- Modélisation et identification du système asservi -- Essais expérimentaux effectués avec l'algorithme BDL dans le cas d'un tracteur -- Comparaison avec les algorithmes d'Astolfi et de Roger Jang -- Essais expérimentaux effectués avec l'algorithme BDL dans le cas d'un tracteur-remorque -- Comparaison avec des versions étendues des algorithmes d'Astolfi et de Roger Jang

Path Following and Motion Control for Articulated Frame Steering Mobile Working Machine Using ROS2

Autonomous vehicles (AVs) have been studied and researched at least since the middle of 19s century, and the interest in these vehicles has grown in the last decade. There are many vehicle types with different steering techniques. Each is designed and manufactured depending on the need to perform specific tasks (for example, transporting passengers, transporting goods, and doing heavy duties like cutting trees, digging earth, and harvesting crops). This thesis highlights the autonomous articulated frame steering (AFS) heavy-duty mobile working machines and aims to address the problems of autonomizing the AFS machine with basic autonomy requirements, which makes the machine move without the need for human direct and indirect control. The working environment (like mines, forests, and construction sites), where heavy-duty machines are used to perform some tasks, requires an expert machine operator to drive it and control its manipulator, which increases the operator’s workload. However, due to the working environment’s limited area, the machine mostly has repetitive tasks that include following the same paths; therefore, we proposed implementing a path-following control system that could be used to help the operator by reducing the work amount. The proposed path following is based on controlling the vehicle’s position and orientation to match the desired positions and orientation on a specified path where the position’s lateral error and orientation error are minimized to zero while the vehicle follows the given path. The implemented control system is divided into many subsystems; each is responsible for a specific task, and to communicate between them we used the Robot Operating System ROS2. In this thesis, we are focusing on two of these subsystems. The first subsystem, called path following that, generates linear and angular velocities needed to make the machine follow the path. The other subsystem, called motion control, is responsible for converting the linear and angular velocities to machine commands (gear, steering, gas) and controls the machine’s acceleration and steering angle. The implemented path-following control system required understanding the machine’s kinematics and modeling the steering system. The implemented system is tested first using an AFS robot in a simulation environment, then tested on a real AFS heavy-duty machine owned by Tampere university. Moreover, the tests repeated for another path following based on the modified pure pursuit technique provided by ROS2 navigation for compression and evaluation purposes

A driver model with supervision aspects

Human driver compensatory reactions -- Driver intelligence and Path Tracking (supervision) -- From human decision making to design of control level -- Vehicle models -- History of driver models -- Controller for car-like mobile robots -- Control problems of vehicle cartesian coordiantes -- Independent speed control with geometric lateral-offset tracking -- Kinematics dynamics and control of a car-like mobile robot -- Looking ahead path tracking of a car-like mobile robot -- Geometric lateral-offset tracking and speed control of a car-like mobile robot -- Equations of motion of a car-like robot using autolev programming

Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks

Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research. Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case. In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn\u27t the trailer it trained on