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

Self-tuning controller for farm tractor guidance

A global position-sensing system using navigational technology has been researched and applied to control a farm tractor in field conditions. Besides guiding a tractor in conservation tillage systems, navigational positioning systems can be used to generate field maps which can help in the application of chemicals and in visualizing variation of soil and crop conditions;A tractor dynamic simulator was developed by using a semi-recursive formulation which uses the variational vector approach and relative coordinates in Cartesian space. Typical joints were formulated for automatic assembly of equations of motion, and cut-joint Jacobians were used to handle with a closed-loop mechanism;A self-tuning steering controller, which can be used for all non-contact types of the positioning systems, was designed for tractor guidance systems. A simple two degrees-of-freedom model of a tractor was chosen to develop a prediction model used in recursive least-squares parameter estimation. A variable forgetting factor was implemented, and its algorithm was modified to cope with time-varying nonlinear systems. The self-tuning steering controller based upon minimum variance control was tested and verified by using the tractor dynamic simulator. Test paths used were a circular path with a radius of 36 m and a composite path which consisted of two lane-change and continuous sinusoidal maneuvers. The test speeds considered were in the range of 0-18 km/h;The study found: (1) an accurate position-sensing system is the most important factor to control the tractor path within ±5 cm of the desired path; (2) a fast sampling can be achieved in practical applications because the execution time of the controller program was about 5 msec; (3) the self-tuning controller that can be used to guide a tractor with any non-contact types of positioning system can measure the position or the position error with respect to the desired path; (4) with the sampling interval 0.1 and 0.2 seconds, the controller could control the tractor position within ±5 cm of the desired path at all test speeds. (Abstract shortened with permission of author.

Event and Time-Triggered Control Module Layers for Individual Robot Control Architectures of Unmanned Agricultural Ground Vehicles

Automation in the agriculture sector has increased to an extent where the accompanying methods for unmanned field management are becoming more economically viable. This manifests in the industry’s recent presentation of conceptual cab-less machines that perform all field operations under the high-level task control of a single remote operator. A dramatic change in the overall workflow for field tasks that historically assumed the presence of a human in the immediate vicinity of the work is predicted. This shift in the entire approach to farm machinery work provides producers increased control and productivity over high-level tasks and less distraction from operating individual machine actuators and implements. The final implication is decreased mechanical complexity of the cab-less field machines from their manned counter types. An Unmanned Agricultural Ground Vehicle (UAGV) electric platform received a portable control module layer (CML) which was modular and able to accept higher-level mission commands while returning system states to high-level tasks. The simplicity of this system was shown by its entire implementation running on microcontrollers networked on a Time-Triggered Controller Area Network (TTCAN) bus. A basic form of user input and output was added to the system to demonstrate a simple instance of sub-system integration. In this work, all major levels of design and implementation are examined in detail, revealing the ‘why’ and ‘how’ of each subsystem. System design philosophy is highlighted from the beginning. A state-space feedback steering controller was implemented on the machine utilizing a basic steering model found in literature. Finally, system performance is evaluated from the perspectives of a number of disciplines including: embedded systems software design, control systems, and robot control architecture. Recommendations for formalized UAGV system modeling, estimation, and control are discussed for the continuation of research in simplified low-cost machines for in-field task automation. Additional recommendations for future time-triggered CML experiments in bus robustness and redundancy are discussed. The work presented is foundational in the shift from event-triggered communications towards time-triggered CMLs for unmanned agricultural machinery and is a front-to-back demonstration of time-triggered design. Advisor: Santosh K. Pitl

Motion Planning for Autonomous Grain Carts

In crop harvesting, a combine travels around in the field to collect grain while a grain cart commutes between the combine and a semi-trailer by the roadside to transport the grain. There are several problems associated with human-operated grain carts: labor shortage and increasing labor cost, operational imprecision and inefficiency as well as safety hazards. All of these problems can potentially be addressed if grain carts were autonomous. To facilitate full autonomy of grain carts, this study develops a motion planning algorithm, featuring a novel integration of Artificial Potential Field (APF) with Fuzzy Logic Control (FLC). In addition, this study proposes a high-level software and hardware solution to building the navigation systems for implementing the developed motion planning algorithm on autonomous grain carts, covering sensor selection, communication options, control technique and actuation plan. A set of simulation tests featuring the comparison between the proposed APF+FLC planner and a simple APF planner were carried out in MatLab Simulink. The simulation tests demonstrated that the proposed motion planning algorithm and the associated task scheduling strategy could promptly direct an autonomous grain cart to intelligently perform the logistical tasks in harvesting operations where unharvested crops were the only obstacles as well as when random static or dynamic obstacles existed, outperforming the simple APF planner in trajectory length and smoothness by roughly 15% to 20%. In addition, another set of simulation tests comparing the proposed APF+FLC planner with a Vector- Field-Histogram (VFH) planner were conducted to further evaluate the performance of the proposed algorithm. It was shown that although the VFH planner tended to plan smoother paths, the APF+FLC planner was superior in terms of generating shorter paths with less computational cost (shorter and less both by as much as 60%). Results of the two sets of simulation tests verified the effectiveness, robustness, efficiency and computational ease of the proposed motion planning algorithm. Following the simulation tests, a set of mobile robot tests implementing the proposed navigation solution were conducted, in which the proposed algorithm was effective in directing the grain cart to intelligently accomplish the logistical tasks in harvest operations. Additionally, the mobile robot tests included a variety of more general obstacle avoidance cases, in which the proposed algorithm was always effective in leading the robot to efficiently accomplish the navigation tasks, outperforming a simple APF planner in trajectory length by as much as 25% and in smoothness by as much as three times. The mobile robot tests verified the effectiveness and practicality of the proposed navigation solution as well as the effectiveness, robustness, and especially efficiency of the proposed motion planning algorithm

Investigation of integrated control of articulated heavy vehicle using scaled multi-body dynamic model

Heavy vehicle handling control systems have proven to be an efficient way of reducing road accidents and improving road traffic safety. Testing these control systems on heavy vehicles can be expensive and unsafe. Meanwhile, the scaled model has proven a secure and inexpensive way of designing and deploying vehicle dynamics control. However, the scaled model's mathematical modelling has been mainly limited to the bicycle model, reducing the scope of exploring the handling dynamics. This study presents an innovative way of modelling a scaled tractor semi-trailer using multi-body dynamics software and testing control systems through co-simulation to help develop new control systems safely and inexpensively for improving road traffic safety. In this research, modelling the scaled model of an articulated vehicle was simulated on MSC ADAMS/View, which extends the mathematical model to 168 degrees of freedom. A 1/14 physical model was used to validate the simulation model and co-simulation has been established between MSC ADAMS/View and MATLAB to investigate the control of a scaled model built on MSC ADAMS/View with a developed control system built on MATLAB/Simulink. The scaled model is a 1/14 Scania R620 articulated lorry manufactured by TAMIYA. Different parameters of the scaled model have been measured and used as inputs to the simulation model. MSC ADAMS/View was used to model the vehicle and to capture its response. The results were validated through physical tests, so a microcontroller was added to the physical model with different accelerometers to control and record the vehicle's motion instead of the existing radio control. Co-simulation has been implemented using two different control schemes, which have been built and compared against each other. The first control scheme is the electronic stability control system only. The second one is an integrated control system which combines the active front steering with the electronic stability control scheme. The main target of the developed control systems is to stabilise the vehicle through manoeuvres using the Fuzzy logic methodology. The study's main findings are that the experimental results show reasonable similarity to the simulation results, although there are minor differences. The physical validation of the simulation model indicates that it is possible to model a scaled model using multi-body dynamics software with specific considerations. Also, the results give a good understanding of the performance of heavy vehicles. Finally, using the co-simulation implemented using two different control schemes proves that the control can be developed using the scaled model. The proposed control method has been shown to be useful in developing the stability of the vehicle. It enhances the yaw rate for both tractor-trailer by around 25% and the lateral acceleration by around 20% at manoeuvres. Also, the control can be tuned easily using MATLAB. Meanwhile, the electronic stability control scheme gives better performance than the combined active front steering and electronic stability control scheme

Development of a low level autonomous machine

An autonomous machine is a machine that can navigate through its environment without human interactions. These machines use sensors to sense the environment and have computing abilities for receiving and interpreting the sensory data as well as for controlling their displacement. At the University of Saskatchewan (Saskatoon, Canada), a low level autonomous machine was developed. This low level machine was the sensor system for an autonomous machine. The machine was capable of sensing the environment and carrying out actions based on commands sent to it. This machine provided a sensing and control layer, but the path planning (decision making) part of the autonomous machine was not developed.This autonomous machine was developed on a Case IH DX 34H tractor with the purpose of providing a machine for testing software and sensors in a true agricultural environment. The tractor was equipped with sensors capable of sensing the speed and heading of the tractor. A control architecture was developed that received input commands from a human or computer in the form of a target heading and speed. The control architecture then adjusted controls on the tractor to make the tractor reach and maintain the target heading and speed until a new command was provided. The tractor was capable of being used in all kinds of weather, although some minor issues arose when testing in rain and snow. The sensor platform developed was found to be insufficient for proper control. The control structure appeared to work correctly, but was hindered by the poor sensor platform performance

Efficient Automated Driving Strategies Leveraging Anticipation and Optimal Control

Automated vehicles and advanced driver assistance systems bring computation, sensing, and communication technologies that exceed human abilities in some ways. For example, automated vehicles may sense a panorama all at once, do not suffer from human impairments and distractions, and could wirelessly communicate precise data with neighboring vehicles. Prototype and commercial deployments have demonstrated the capability to relieve human operators of some driving tasks up to and including fully autonomous taxi rides in some areas. The ultimate impact of this technology’s large-scale market penetration on energy efficiency remains unclear, with potential negative factors like road use by empty vehicles competing with positive ones like automatic eco-driving. Fundamentally enabled by historic and look-ahead data, this dissertation addresses the use of automated driving and driver assistance to optimize vehicle motion for energy efficiency. Facets of this problem include car following, co-optimized acceleration and lane change planning, and collaborative multi-agent guidance. Optimal control, especially model predictive control, is used extensively to improve energy efficiency while maintaining safe and timely driving via constraints. Techniques including chance constraints and mixed integer programming help overcome uncertainty and non-convexity challenges. Extensions of these techniques to tractor trailers on sloping roads are provided by making use of linear parameter-varying models. To approach the wheel-input energy eco-driving problem over generally shaped sloping roads with the computational potential for closed-loop implementation, a linear programming formulation is constructed. Distributed and collaborative techniques that enable connected and automated vehicles to accommodate their neighbors in traffic are also explored and compared to centralized control. Using simulations and vehicle-in-the-loop car following experiments, the proposed algorithms are benchmarked against others that do not make use of look-ahead information

A robotic platform for precision agriculture and applications

Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.Le tecniche agricole sono state migliorate nel corso dei secoli per soddisfare la crescente domanda di aumento della popolazione mondiale. I recenti progressi tecnologici in termini di piattaforme robotiche possono essere sfruttati in questo contesto. Poiché la gestione del frutteto è una delle applicazioni più impegnative, a causa della sua struttura arborea e della necessaria interazione con l'ambiente, è stata oggetto di ricerca per fornire una soluzione personalizzata che sviluppi un nuovo concetto di veicolo agricolo. Il risultato si è concretizzato in un veicolo cingolato leggero, capace di effettuare una navigazione autonoma sia nello scenario di pieno campo che all'interno dei frutteti (navigazione interfilare). La progettazione meccanica, insieme all'implementazione del software, sono stati dettagliati per evidenziarne i punti di forza, accanto ad alcuni ulteriori miglioramenti previsti per incrementarne le prestazioni complessive. I test di stabilità statica hanno dimostrato che il veicolo può resistere a ripidi pendii. Sono stati inoltre studiati miglioramenti per affinare la stima dello slittamento che si verifica durante le manovre di svolta, tipico dei veicoli cingolati. L'architettura software è stata implementata utilizzando il framework Robot Operating System (ROS), in modo da sfruttare i pacchetti disponibili relativi a componenti base, come le interfacce dei sensori, e consentendo al contempo un'implementazione personalizzata degli algoritmi di navigazione sviluppati. I test in condizioni reali all'interno dei frutteti sperimentali dell'università hanno dimostrato la robustezza e la stabilità della soluzione con oltre 800 ore di lavoro sul campo. Il veicolo ha permesso di attivare e svolgere un'ampia gamma di attività agricole in maniera autonoma, come l'irrorazione, la falciatura e la raccolta di dati sul campo. Questi ultimi possono essere sfruttati per stimare automaticamente le proprietà più rilevanti del frutteto, come il conteggio e la calibratura dei frutti, la stima delle proprietà della chioma e la raccolta autonoma dei frutti con stime post-raccolta

Commercial Vehicle Research Buggy For Active Driver Assistance Systems

This is the Final Design Report for Daimtronics, a senior project team sponsored by Professor Charles Birdsong of Cal Poly and by Daimler Trucks North America. This team integrated mechatronic systems into a scale semi-truck chassis using existing mechanical and software systems from three separate Cal Poly senior projects over the recent years: Daimscale, MicroLaren, and ProgreSSIV. The goal was to have a user-friendly platform capable of executing autonomous driving algorithms that are programmable at a high level in Simulink and Robotic Operating System (ROS). Advanced driver assistance and autonomous vehicle algorithms were not within the scope of this project, but the capability to upload the platform with such software was. Through research on existing products and technologies in the field today, as well as through communication with the sponsors, Daimtronics has compiled a list of customer needs and resulting engineering specifications that will verify whether the needs are met or not. Included is both the preliminary design direction, encompassing the selection of a motor, a computing platform and a middleware framework, as well as the final design direction as the project evolved. The sensor suite for object detection and detailed plans for the integration of the electronic, computing, and mechanical components are described. The proposed and final design of the motherboard integrating the electronic and computing platforms of the system is detailed. A description of the current state of the project is included, as well as suggested next steps for future teams who will be working on this platform. A timeline of key deliverables and their due dates throughout the 2018-2019 academic school year is included