Georg Schildbach

• Professor
• University of Lübeck

Learning-based controllers are often purposefully kept out of real-world applications due to concerns about their safety and reliability. We explore how state-of-the-art world models in Model-Based Reinforcement Learning can be utilized beyond the training phase to ensure a deployed policy only operates within regions of the state-space it is suffi...

This paper presents a prototype for an aerial mobile network relay in the setting of pre-hospital emergency care. The requirements for such an unmanned aerial vehicle (UAV) are summarized and used to develop a hardware prototype. Gaussian processes and Bayesian optimization are implemented to find an optimal location for the aerial relay. The aeria...

The advent of 5G technology has facilitated the adoption of private cellular networks in industrial settings. Ensuring reliable coverage while maintaining certain requirements at its boundaries is crucial for successful deployment yet challenging without extensive measurements. In this article, we propose the leveraging of unmanned aerial vehicles...

This paper considers the application of Model Predictive Control (MPC) to a weighted coverage path planning (WCPP) problem. The problem appears in a wide range of practical applications, such as search and rescue (SAR) missions. The basic setup is that one (or multiple) agents can move around a given search space and collect rewards from a given sp...

The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices and ensure seamless integration into human-dominated environments. This paper presents a compa...

Ensuring the safe operation of autonomous systems is a critical challenge that demands the development of sophisticated control strategies. This article proposes a safe control architecture (SCA) that employs a supervisor model predictive control (MPC) (supervisor) strategy to ensure the persistent satisfaction of state and input constraints. The s...

Automated and autonomous driving has made a significatnt technological leap over the past decade. In this process, the complexity of algorithms used for vehicle control has grown significantly. Model Predictive Control (MPC) is a prominent example, which has gained enormous popularity and is now widely used for vehicle motion planning and control....

In light of the rapid evolution of Artificial Intelligence (AI), a growing number of researchers are investigating the use of Artificial Neural Networks (ANNs) to enhance first-principle Vehicle Models (VMs) or potentially replace them altogether. This paper investigates how AI can be used optimally to identify a VM in the context of a specific cas...

This paper addresses the problem of traffic prediction and control of autonomous vehicles on highways. A modified Interacting Multiple Model Kalman filter algorithm is applied to predict the motion behavior of the traffic participants by considering their interactions. A scenario generation component is used to produce plausible scenarios of the ve...

The global trend indicates that overall wind energy production, both onshore and offshore, will increase drastically in the next decade. Therefore, presently, much effort is focused on optimizing the operation and maintenance of wind turbines, since these are quite challenging and cost‐intensive. To aid or even completely fulfill a specific inspect...

This article addresses the problem of traffic prediction and control of autonomous vehicles on highways. An interacting multiple model Kalman filter (IMM-KF)-related algorithm is applied to predict the motion behavior of the traffic participants by considering their interactions. A scenario generation component is used to produce plausible scenario...

This work develops a first Model Predictive Control for European Space Agencies 3-dof free-floating platform. The challenges of the platform are the on/off thrusters, which cannot be actuated continuously and which are subject to certain timing constraints. This work compares penalty-term, Linear Complementarity Constraints, and classical Mixed Int...

In this study, we are concerned with autonomous driving missions when a static obstacle blocks a given reference trajectory. To provide a realistic control design, we employ a model predictive control (MPC) utilizing nonlinear state-space dynamic models of a car with linear tire forces, allowing for optimal path planning and tracking to overtake th...

In this study, we are concerned with autonomous driving missions when a static obstacle blocks a given reference trajectory. To provide a realistic control design, we employ a model predictive control (MPC) utilizing nonlinear state-space dynamic models of a car with linear tire forces, allowing for optimal path planning and tracking to overtake th...

This paper presents an Adaptive Model Predictive Controller (AMPC) for vehicle trajectory tracking. The proposed approach combines Model Predictive Control (MPC) with an online parameter learning algorithm based on a Gaussian Process Regression (GPR). The goal is to improve the tracking accuracy caused by model errors and condition changes. The veh...

This paper introduces a geometric algorithm for Continuous Collision Detection (CCD). It can be used for so-called Dubins paths, which are composed of arcs and straight lines. The CCD approach checks for an overlap between the area covered by the vehicle during a full arc and the polytopic obstacles. Previous work has already demonstrated the poten...

To deal with the problem of optimal path planning in 2D space, this paper introduces a new toolbox named "Navigation with Polytopes" and explains the algorithms behind it. The toolbox allows one to create a polytopic map from a standard grid map, search for an optimal corridor, and plan a safe B-spline reference path used for mobile robot navigatio...

This work introduces a ROS2-based software stack for a six wheeled rover with quasi-omnidirectional locomotion system. It includes a generic Ackerman locomotion to perform any planar rigid body twist with the rover, while taking into account the motor constraints. To improve autonomous path tracking, the existing pure pursuit controller is extended...

This paper proposes a control architecture for autonomous lane keeping by a vehicle. In this paper, the vehicle dynamics consist of two parts: lateral and longitudinal dynamics. Therefore, the control architecture comprises two subsequent controllers. A longitudinal model predictive control (MPC) makes the vehicle track the desired longitudinal spe...

This paper proposes a control architecture for autonomous lane keeping by a vehicle. In this paper, the vehicle dynamics consist of two parts: lateral and longitudinal dynamics. Therefore, the control architecture comprises two subsequent controllers. A longitudinal model predictive control (MPC) makes the vehicle track the desired longitudinal spe...

This paper proposes a Robust Safe Control Architecture (RSCA) for safe-decision making. The system to be controlled is a vehicle in the presence of bounded disturbances. The RSCA consists of two parts: a Supervisor MPC and a Controller MPC. Both the Supervisor and the Controller are tube MPCs (TMPCs). The Supervisor MPC provides a safety certificat...

In this paper, a Deep Neural Network is trained using Reinforcement Learning in order to drift on arbitrary trajectories which are defined by a sequence of waypoints. In a first step, a highly accurate vehicle simulation is used for the training process. Then, the obtained policy is refined and validated on a self-built model car. The chosen reward...

Developing reusable software for mobile robots is still challenging. Even more so for swarm robots, despite the desired simplicity of the robot controllers. Prototyping and experimenting are difficult due to the multi-robot setting and often require robot-robot communication. Also, the diversity of swarm robot hardware platforms increases the need...

We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. In our approach, an automatic guided vehicle is equipped with two LiDAR sensors and one frontal RGB camera and learns to perform a targeted navigation task. The challenges reside in the sparseness of positive samples for learning, mult...

We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. The automatic guided vehicle is equipped with LiDAR and frontal RGB sensors and learns to reach underneath the target dolly. The challenges reside in the sparseness of positive samples for learning, multi-modal sensor perception with p...

This paper presents a novel, safe control architecture (SCA) for controlling an important class of systems: safety-critical systems. Ensuring the safety of control decisions has always been a challenge in automatic control. The proposed SCA aims to address this challenge by using a Model Predictive Controller (MPC) that acts as a supervisor for the...

This paper presents a new approach for safety verification of self-driving systems. A statistical approach to verification is often prohibitive, so a recent trend has been to consider synthetically generated scenarios based on predefined parameters. Instead of covering a large fraction of the parameter space, however, this paper proposes an approac...

Research on automated vehicles has experienced an explosive growth over the past decade. A main obstacle to their practical realization, however, is a convincing safety concept. This question becomes ever more important as more sophisticated algorithms are used and the vehicle automation level increases. The field of functional safety offers a syst...

This paper presents a novel design of control algorithms for lane change assistance and autonomous driving on highways, based on recent results in Scenario Model Predictive Control (SCMPC). The basic idea is to account for the uncertainty in the traffic environment by a small number of future scenarios, which is intuitive and computationally effici...

Model Predictive Control (MPC) is a powerful technique to control nonlinear, multi-input multi-output systems with input and state constraints. It has previously been considered for trajectory tracking control of automated vehicles in many projects. However, MPC faces several challenges in practice, mainly regarding computation time and difficulty...

Model Predictive Control (MPC) is a powerful technique to control nonlinear, multi-input multi-output systems with input and state constraints. It has previously been considered for trajectory tracking control of automated vehicles in many projects. However, MPC faces several challenges in practice, mainly regarding computation time and difficulty...

Collisions at intersections account for about 40% of all car accidents and for about 20% of all traffic fatalities in the United States. The main cause is human error in recognition and decision making. Active safety systems have thus a great potential for increasing vehicle safety at intersections. They may issue warnings to the driver or assume c...

State-of-the-art autonomous cars use various algorithms for path planning in different environments. The design of these algorithms is difficult when the nonlinear and the nonholonomic aspect of the vehicle dynamics are dominant. These aspects are small at high speeds and for simple maneuvers at low speeds, so effective algorithms exist. However, p...

Policies for managing multi-echelon supply chains can be considered mathematically as large-scale dynamic programs, affected by uncertainty and incomplete information. Except for a few special cases, optimal solutions are computationally intractable for systems of realistic size. This paper proposes a novel approximation scheme using scenario-based...

Two recent predictive control approaches for constrained systems subject to uncertainty are reviewed. The first one, named scenario MPC, is best suited for stochastic systems where a certain share of constraint violations is tolerated and rewarded. The approach is able to control precisely the share of violations that occur during closed loop opera...

Many control design problems subject to uncertainty can be cast as chance constrained optimization programs. The Scenario Approach provides an intuitive way to address these problems by replacing the chance constraint with a finite number of sampled constraints (scenarios). The sample size critically depends on the so-called Helly's dimension, whic...

This paper presents a new controller for prevention of unintended roadway departures using model predictive control (MPC). The uncertainty with the driver's behavior is taken into account as the Gaussian disturbance. Correspondingly , we impose a lower bound on the probability of the vehicle remaining within the lane. Using current information of t...

This paper presents a practicable Scenario-Based Model Predictive Control (Scenario MPC) approach for linear, time-varying systems with additive disturbances. Robust MPC propagates uncertainty through the dynamics based on uncertainty sets and Stochastic MPC by multi-variable convolutions of probability distributions. The idea of Scenario MPC is to...

This paper considers the problem of path planning for autonomous ground vehicles on highways with regular traffic. The goal is to select a desired trajectory from a set of parameterized candidate trajectories such that some criterion is optimized. This selection is subject to avoiding collisions, respecting the traffic rules, and eliciting smooth b...

All-way stop intersections are widely used for traffic management in North America. Therefore, modeling and control of vehicle behavior at stop intersections is fundamental for driver assistance systems and autonomous driving. This paper presents a method to predict the maneuvers performed by vehicles at arbitrary all-way stop intersections, using...

This paper presents a new algorithm for detecting the safety of lane changes on highways and for computing safe lane change trajectories. This task is considered as a building block for driver assistance systems and autonomous cars. The presented algorithm is based on recent results in Scenario Model Predictive Control (SCMPC). It accounts for the...

We study the use of kinematic and dynamic vehicle models for model-based control design used in autonomous driving. In particular, we analyze the statistics of the forecast error of these two models by using experimental data. In addition, we study the effect of discretization on forecast error. We use the results of the first part to motivate the...

Driving requires forecasts. Forecasted movements of objects in the driving scene are uncertain. Inevitably, decision and control algorithms for autonomous driving need to cope with such uncertain forecasts. In assisted driving, the uncertainty in the human/vehicle interaction further increases the complexity of the control design task. Our research...

This paper is concerned with the design of a linear control law for a linear system with stationary additive disturbances. The objective is to find a state feedback gain that minimizes a quadratic stage cost function, while observing chance constraints on the input and/or the state. Unlike most of the previous literature, the chance constraints (an...

We consider Stochastic Model Predictive Control (SMPC) for constrained linear systems with additive disturbance, under affine disturbance feedback (ADF) policies. One approach to solve the chance-constrained optimization problem associated with the SMPC formulation is randomization, where the chance constraints are replaced by a number of sampled h...

Many practical applications of control require that constraints on the inputs and states of the system be respected, while optimizing some performance criterion. In the presence of model uncertainties or disturbances, for many control applications it suffices to keep the state constraints at least for a prescribed share of the time, as e.g. in buil...

Heating, ventilation and air conditioning (HVAC) systems regulate comfort levels in buildings, but also consume a large amount of energy, which makes them an attractive target for efficiency improvements. In this paper, a novel technique called Randomized Model Predictive Control (RMPC) is investigated to improve the control of existing HVAC system...

This paper is concerned with the design of linear state feedback control laws for linear systems with additive Gaussian disturbances. The objective is to find the feedback gain that minimizes a quadratic cost function in closed-loop operation, while observing chance constraints on the input and/or the state. It is shown that this problem can be cas...

This paper is concerned with the design of state-feedback control laws for linear time invariant systems that are subject to stochastic additive disturbances, and probabilistic constraints on the states. The design is based on a stochastic Model Predictive Control (MPC) approach, for which a randomization technique is applied in order to find a sub...

The scenario-based optimization approach (`scenario approach') provides an intuitive way of approximating the solution to chance-constrained optimization programs, based on finding the optimal solution under a finite number of sampled outcomes of the uncertainty (`scenarios'). A key merit of this approach is that it neither assumes knowledge of the...

Research on sub-optimal Model Predictive Control (MPC) has led to a variety of optimization methods based on explicit or online approaches, or combinations thereof. Its foremost aim is to guarantee essential controller properties, i.e. recursive feasibility, stability, and robustness, at reduced and predictable computational cost, i.e. computation...

Research on sub-optimal Model Predictive Control (MPC) has led to a variety of optimization methods based on explicit or online approaches, or combinations thereof. Its foremost aim is to guarantee essential controller properties, i.e. recursive feasibility, stability, and robustness, at reduced and predictable computational cost, i.e. computation...