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 sufficiently familiar with. This is achieved by continuously monitoring discrepancies between a world model’s predictions and observed system behavior during inference. It allows for triggering appropriate measures, such as an emergency stop, once an error threshold is surpassed. This does not require any task-specific knowledge and is thus universally applicable. Simulated experiments on established robot control tasks show the effectiveness of this method, recognizing changes in local robot geometry and global gravitational magnitude. Real-world experiments using an agile quadcopter further demonstrate the benefits of this approach by detecting unexpected forces acting on the vehicle. These results indicate how even in new and adverse conditions, safe and reliable operation of otherwise unpredictable learning-based controllers can be achieved.

This work introduces the idea of skill-based personalization for Advanced Driver Assistance Systems, aiming to address the limitations of traditional imitation-based personalization methods. Therefore, an innovative, adaptive path planning approach is developed as a crucial intermediate step in realizing this concept. This approach is exemplified through automated parking. For this purpose, the Hybrid$\mathrm{A}^{*}$and the Elastic-Band methods were integrated and modified to accommodate flexible target positions and incorporate additional Key Performance Indicators (KPIs) relevant for personalized parking algorithms in their cost functions. Additionally, two novel shortcut algorithms are proposed to address some of the limitations in adjusting these KPIs. As a result, a path planner is developed that is capable of producing customized paths aligned with the user's specific skills and preferences.

In this work, we provide deterministic error bounds for the actual state evolution of nonlinear systems that can be embedded in linear parameter-varying (LPV) formulation and steered by model predictive control (MPC). The main novelty concerns the explicit derivation of these deterministic bounds as polytopic tubes using linear differential inclusions (LDIs). We provide exact error formulations compared to other approaches based on linearization schemes that inevitably introduce additional errors and deteriorate performance. The analysis and method are certified by solving the regulation problem of an unbalanced disk.

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 the obstacle. We provide solutions with two different methodologies. Firstly, we solve a nonlinear MPC (NMPC) problem with a nonlinear optimization framework, capable of considering the nonlinear constraints. Secondly, by introducing scheduling signals, we embed the nonlinear dynamics in a linear parameter varying (LPV) representation with adaptive linear constraints for realizing the nonlinear constraints associated with the obstacle. Consequently, an LPVMPC optimization problem can be solved efficiently as a quadratic programming (QP) that constitutes the main novelty of this work. We test the two methods for a challenging obstacle avoidance task and provide qualitative comparisons. The LPVMPC shows a significant reduction in terms of the computational burden at the expense of a slight loss of performance.

This paper firstly presents our optimal path planning algorithm within a$2\mathrm{D}$non-convex, polytopic region defined as a sequence of connected convex polytopes. The path is a B-spline curve but being parametrized with its equivalent Bézier representation. By doing this, the local convexity bound of each curve's interval is significantly tighter. Thus, it allows many more possibilities for constraining the entire curve to remain inside the region by using only linear constraints on the control points of the curve. We further guarantee the existence of the valid path by pointing out an algebraic solution. We integrate the algorithm, together with our previously published results, into the Navigation with polytopes toolbox which can be used as a global path planner, compatible with ROS navigation tools. It provides a framework for constructing a polytope map from a standard occupancy gridmap, searching for an appropriate sequence of connected polytopes and finally, planning a minimal-length path with different options on B-spline or Bézier parametrizations. The validation and comparison with existing methods are done using gridmaps collected under Gazebo simulations and real experiments.

This paper proposes an architecture for integrated decision-making, motion planning, and control in autonomous highway driving. The approach anticipates, to some degree, interactions between traffic participants and their reactive behavior to the actions of the autonomous vehicle (AV). To this end, we utilize an interaction-aware traffic prediction model to identify likely scenarios resulting from the current traffic scene, depending on the AV’s tactical decision options, which are evaluated by an ensemble of Scenario-based Model Predictive Controllers to decide on lane-changing maneuvers. We conduct a validation of two versions of the scenario generation using traffic data and demonstrate the combined architecture in a simulation study.

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 potential benefits over other methods, which perform collision checks at discrete sample points along the path. Compared to the previous CCD implementation, the proposed geometric algorithm requires only on elementary functions and does not depend on external libraries, e.g., for numerical optimization. A numerical study demonstrates the numerical speed-up, for elementary collision checks and in combination with a basic Rapidly-exploring Random Tree (RRT) path planner.

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 function is inspired by the scoring process of real life drifting competitions. It is kept simple and thus applicable to very general scenarios. The experimental results demonstrate that a relatively small network, given only a few measurements and control inputs, already achieves an outstanding performance. In simulation, the learned controller is able to reliably hold a steady state drift. Moreover, it is capable of generalizing to arbitrary, previously unknown trajectories and different driving conditions. After transferring the learned controller to the model car, it also performs surprisingly well given the physical constraints.

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 for hardware-independent software concepts. The main advantages of the commonly used robot software architecture ROS 2 are modularity and platform independence. We propose a new ROS 2 package, ROS2sWARM, for applications of swarm robotics that provides a library of ready-to-use swarm behavioral primitives. We show the successful application of our approach on three different platforms, the TurtleBot3 Burger, the TurtleBot3 Waffle Pi, and the Jackal UGV, and with a set of different behavioral primitives, such as aggregation, dispersion, and collective decision-making. The proposed approach is easy to maintain, extendable, and has good potential for simplifying swarm robotics experiments in future applications.

We propose a 2D path planning algorithm in a non-convex workspace defined as a sequence of connected convex polytopes. The reference path is parameterized as a B-spline curve, which is guaranteed to entirely remain within the workspace by exploiting the local convexity property and by formulating linear constraints on the control points of the B-spline. The novelties of the paper lie in the use of the equivalent Bézier representation of the B-spline curve, which significantly reduces the conservatism in the local convexity bound and in the integration of these constraints into a convex quadratic optimization problem, which minimizes the curve length. The algorithm is successfully validated in both simulations and experiments, by providing obstacle-free reference paths on real occupancy grid maps obtained from the laser scan data of a mobile robot platform.

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, path planning for more complex maneuvers at low speeds, especially in tight and cluttered environments, remains a difficult challenge. This paper proposes a new approach to this problem. The presented algorithm performs a tree-search on a discretized state space using dynamic programming. It is shown in simulation and experiments that even complicated paths can be computed very efficiently. Since a path is composed of a sequence of simple arcs, it is easy to track by a linear controller.

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 control of the vehicle in critical situations. Most approaches in current research rely on the assumption that all vehicles at the intersection are controllable, and/or they can be coordinated by a central intersection manager. This paper considers the case of a single controllable ego vehicle surrounded by several uncontrollable target vehicles, without communication. Only a map with the current position and velocity of the target vehicles are assumed to be known, but no pre-defined crossing order is given. A Robust Model Predictive Control strategy is designed for finding safe gaps in the crossing traffic, and for planning optimal trajectories to maximize the ego vehicle's efficiency and driver comfort. It is shown that its performance can be enhanced by Affine Disturbance Feedback. The algorithm is tested in several simulation scenarios and implemented on a test vehicle for experimental validation.

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 design of a controller for an autonomous vehicle using model predictive control (MPC) and a simple kinematic bicycle model. The proposed approach is less computationally expensive than existing methods which use vehicle tire models. Moreover it can be implemented at low vehicle speeds where tire models become singular. Experimental results show the effectiveness of the proposed approach at various speeds on windy roads.

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 uncertainty in the traffic environment via a small number of future scenarios, which can be generated by any model-based or data-based approach. The paper describes the SCMPC design as well as the integration with scenario-based traffic predictions. The design procedure is simple and can be generalized to other control situations. An extensive case study demonstrates the effectiveness of the proposed SCMPC algorithm and its performance in lane change situations.

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 noisy sensor data. This is required for an autonomous vehicle to decide when to enter the intersection, or for a driver assistance system to decide when to issue a collision warning to the driver. The problem is divided into two components. The first component estimates the maneuver intention of the drivers by means of a naïve Bayesian filter. The second component predicts the order in which the vehicles will enter the intersection by means of a kinematic feedback model. Both algorithms are evaluated using real world data collected with laser sensors mounted on a vehicle. The Bayesian filter is successfully applied to intersections of different sizes and geometries. We show that the filter identifies maneuvers earlier than a deterministic reference model.

This paper considers the efficient operation of shared mobility systems via the combination of intelligent routing decisions for staff-based vehicle redistribution and real-time price incentives for customers. The approach is applied to London's Barclays Cycle Hire scheme, which the authors have simulated based on historical data. Using model-based predictive control principles, dynamically varying rewards are computed and offered to customers carrying out journeys, based on the current and predicted state of the system. The aim is to encourage them to park bicycles at nearby underused stations, thereby reducing the expected cost of redistributing them using dedicated staff. In parallel, routing directions for redistribution staff are periodically recomputed using a model-based heuristic. It is shown that it is possible to trade off reward payouts to customers against the cost of hiring staff to redistribute bicycles, in order to minimize operating costs for a given desired service level.