Overtaking in Autonomous Racing with Online Refinement of Opponent Behavior Prediction using Gaussian Process

Department of Mechanical EngineeringThis paper addresses an overtaking strategy in autonomous head-to-head racing, by virtue of a learningbased prediction to the opponent vehicle???s behavior. The existing prediction approaches either rely on prior model or off-line learning for opponent behavior, whose accuracy diminishes when the opponent in real racing exhibits different driving style. Motivated by this concern, we proposes an online learningbased prediction algorithm that can adapt to the opponents??? different driving style and refine the prediction during the race. Resorting to Gaussian Process (GP) regressor as the baseline learning model, we leverage several techniques to reduce the data size and computation cost of GP, making the algorithm suitable for online learning and prediction refinement in real time. The effectiveness of the proposed algorithm is demonstrated with different simulation scenarios and compared with the other algorithms in terms of prediction accuracy, computation efficiency, and success rate of overtaking maneuver.ope

Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation

The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision. In this work we study the post-processing techniques applied to sparse 3D point-cloud maps, obtained by feature-based vSLAM algorithms. Map post-processing is split into 2 major steps: 1) noise and outlier removal and 2) upsampling. We evaluate different combinations of known algorithms for outlier removing and upsampling on datasets of real indoor and outdoor environments and identify the most promising combination. We further use it to convert a point-cloud map, obtained by the real UAV performing indoor flight to 3D voxel grid (octo-map) potentially suitable for path planning.Comment: 10 pages, 4 figures, camera-ready version of paper for "The 3rd International Conference on Interactive Collaborative Robotics (ICR 2018)

Cooperative lateral vehicle guidance control for automated vehicles with Steer-by-Wire systems

With the global trend towards automated driving, fault-tolerant onboard power supply systems are introduced into modern vehicles and the level of driving automation is continuously increasing. These advancements contribute to the applicability of Steer-by-Wire systems and the development of automated lateral vehicle guidance control functions. For the market acceptance of automated driving, the lateral vehicle guidance control function must hereby be cooperative, that is it must accept driver interventions. Existing approaches for automated lateral vehicle guidance commonly do not consider driver interventions. If unconsidered in the control loop, the driver intervention is interpreted as an external disturbance that is actively compensated by feedback. This thesis addresses the development of a cooperative lateral vehicle guidance control concept, which enables a true coexistence between manual steering control by the driver and automated steering control. To this end, the subordinate controls of the Steer-by-Wire system for the manual and automated driving mode are initially presented. These include the steering feel generation and steering torque control of the Steer-by-Wire Handwheel Actuator for the manual driving mode, which is structurally extended to a cascade steering position control for the automated driving mode. Subsequently, a superposition control is introduced, which fuses steering torque and position control. The resulting cooperative Handwheel Actuator control achieves precise tracking of the reference steering position in automated driving mode but accepts driver interventions. Thus, the driver can override the active control and experiences a natural steering feel. The transitions hereby are seamless as no blending, gain scheduling or controller output saturation is required. Subsequently, the superimposed lateral vehicle guidance controller for the automated driving mode is described, which computes the reference steering position for the respective Steer-by-Wire controls. In contrast to existing approaches, the plant model equations are rearranged to isolate the vehicle speed dependent dynamics. Thereafter, the concept of inverse nonlinearity control is employed, using a virtual control loop and feedback linearization for an online inversion of the nonlinear plant dynamics. The remaining plant is fully linear and independent of vehicle speed. Consequently, one controller can be synthesized that is valid for all vehicle speeds. The closed and open loop system thereby have the same dynamics independent of vehicle speed, which significantly simplifies control synthesis, analysis, and performance tuning in the vehicle. For considering the future reference path information and constraints on the maximum steering position within the control law, a linear Model Predictive Controller synthesis is selected. The combination of inverse nonlinearity control and linear Model Predictive Controller thus results in a Nonlinear Adaptive Model Predictive Control concept, which makes commonly applied gain scheduling fully obsolete. The controller is structurally extended by a cooperative dynamic feedforward control for considering driver interventions within the control loop. Consequently, the driver can override the active control and seamlessly modify the lateral vehicle motion. A variety of nonlinear simulation analyses and real vehicle tests demonstrate the effectiveness of the proposed control concept

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence

MODEL PREDICTIVE CONTROL OF SKID-STEERED MOBILE ROBOT WITH DEEP LEARNING SYSTEM DYNAMICS

This thesis project presents several model predictive control (MPC) strategies for control of skid-steered mobile robots (SSMRs) using two different combinations of software environment, optimization tool and machine learning framework. The control strategies are tested in WeBots simulator. Spatial-based path following MPC of SSMR with static obstacle avoidance is developed in MATLAB environment with ACADO optimization toolkit using spatial kinematic model of SSMR. It includes static obstacle and border avoidance strategy based on artificial potential fields. Simulations show that the controller is effective at driving SSMR on a track, while avoiding borders and obstacles. Several more MPCs are developed using Python environment, ACADOS optimisation framework, and Pytorch-Casadi integration framework. Two time-domain controllers are made in Python environment, one based on SSMR kinematic model and another based on data-driven state-space model using Pytorch- Casadi framework. Both are setup to reach a goal point in simulation experiment. Experiments show that both versions reliably reach a target point. Standard and data-driven versions of spatial path following MPC are developed. Standard is a reimplementation of MPC designed in MATLAB with modifications to cost function and border avoidance, without static obstacle avoidance. Data-driven path following MPC is an extension of standard variant with state-space model replaced with a hybrid of spatial kinematics and data-driven model. Simulation of both spatial controllers confirm their effectiveness in following reference path

Preview-based techniques for vehicle suspension control: a state-of-the-art review

Abstract Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution for production vehicles. Despite the availability of a significant body of papers on the topic, the literature lacks a comprehensive and up-to-date survey on the variety of proposed techniques for suspension control with road preview, and the comparison of their effectiveness. To cover the gap, this literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview. The main formulations are reported for each control category, and the respective features are critically analysed, together with the most relevant performance indicators. The paper also discusses the effect of the road preview time on the resulting system performance, and identifies control development trends

A prototype energy management system for a solar powered cycle

Bibliography: leaves 92-93