This paper presents a novel approach for time-optimal path parameterization based on reachability analysis for robotic systems with viscous friction in the dynamics and jerk constraints. The main step of the method is the backward propagation of controllable sets through a linear second-order system. In order to avoid the unbounded growth of the number of constraints, the sets are approximated by a ray shooting algorithm. Using a convex relaxation, the required set expansion can be solved with second-order cone programming. Evaluation results for a 6-degree of freedom (DOF) robot arm highlight the advantages of the method for computing jerk-limited trajectories.

In the trajectory planning of automated driving, data-driven statistical artificial intelligence (AI) methods are increasingly established for predicting the emergent behavior of other road users. While these methods achieve exceptional performance in defined datasets, they usually rely on the independent and identically distributed (i.i.d.) assumption and thus tend to be vulnerable to distribution shifts that occur in the real world. In addition, these methods lack explainability due to their black box nature, which poses further challenges in terms of the approval process and social trustworthiness. Therefore, in order to use the capabilities of data-driven statistical AI methods in a reliable and trustworthy manner, the concept of TrustMHE is introduced and investigated in this paper. TrustMHE represents a complementary approach, independent of the underlying AI systems, that combines AI-driven out-of-distribution detection with control-driven moving horizon estimation (MHE) to enable not only detection and monitoring, but also intervention. The effectiveness of the proposed TrustMHE is evaluated and proven in three simulation scenarios.

This paper presents a time-optimal path parameterization (TOPP) method for cooperative multi-arm robotic systems (MARS) manipulating heavy objects with third-order constraints that include jerk, torque rate and wrench rate limits. The method is based on a problem reformulation as a sequential linear program and provides a unified planning approach that is faster than previous convex optimization techniques. The equivalence to a reachability-based TOPP is shown and simulation results for a cooperative MARS consisting of two 7 degree of freedom (DOF) robots and a tightly grasped object with 6 DOFs are provided.

Alongside optimization-based planners, sampling-based approaches are often used in trajectory planning for autonomous driving due to their simplicity. Model predictive path integral control is a framework that builds upon optimization principles while incorporating stochastic sampling of input trajectories. This paper investigates several sampling approaches for trajectory generation. In this context, normalizing flows originating from the field of variational inference are considered for the generation of sampling distributions, as they model transformations of simple to more complex distributions. Accordingly, learning-based normalizing flow models are trained for a more efficient exploration of the input domain for the task at hand. The developed algorithm and the proposed sampling distributions are evaluated in two simulation scenarios.

Trajectory planning in autonomous driving is highly dependent on predicting the emergent behavior of other road users. Learning-based methods are currently showing impressive results in simulation-based challenges, with transformer-based architectures technologically leading the way. Ultimately, however, predictions are needed in the real world. In addition to the shifts from simulation to the real world, many vehicle- and country-specific shifts, i.e. differences in sensor systems, fusion and perception algorithms as well as traffic rules and laws, are on the agenda. Since models that can cover all system setups and design domains at once are not yet foreseeable, model adaptation plays a central role. Therefore, a simulation-based study on transfer learning techniques is conducted on basis of a transformer-based model. Furthermore, the study aims to provide insights into possible trade-offs between computational time and performance to support effective transfers into the real world.

When considering inverters from a thermal point of view, deviations and offsets of the module-included temperature sensors can be encountered across the production series and aging processes over time affect the systems behavior. In the task of the thermal modeling of inverters, online identification provides the ability to adapt to individual system properties. This article focuses on the online learning and adaptation of thermal models for fluid cooled automotive inverters for the purpose of nonlinear state estimation. For this task, a linear thermal network of Cauer-type is designed in combination with numerical parameter fitting. An unscented Kalman filter (UKF) is applied for online identification of the nonlinear parameters in the network. A Savitzky-Golay filter is used to recognize stationary operating points and the value of the identified thermal resistance is stored in a training set. By switching from identification to estimation, a Gaussian process is trained for regression of the thermal resistance and is used for state estimation in a following UKF. Results based on measurement data from a test bench indicate a significant improvement in the estimation in comparison to classical methods and show potential for recognizing system malfunctions.

Recently, data-driven and hybrid control of hydraulic cylinders for excavator assistance functions have been in the focus of many research papers. To ensure an accurate behavior, data-driven controllers and models need a large amount of data to cover all relevant operation regions, which requires a time-consuming data generation process. In this work, we introduce two learning-based methods to enhance the efficiency of this procedure: a static learning method and an active learning method. Both methods reduce the amount of required data to learn a hydraulic inverse actuation model. Compared to previous collection methods, the required data was reduced by factor 7.5, while the information content of the dataset remains nearly the same.

This paper presents a new control scheme for cooperative dual-arm robots manipulating heavy objects. The proposed method uses the full dynamical model of the kinematically coupled robot system and builds on a hierarchical quadratic programming (HQP) formulation to enforce dynamical inequality constraints such as joint torques or internal loads. This ensures optimal tracking of an object trajectory, while additional objectives with lower priority are optimized on the prior solution space. Therefore, the redundancy of the inherent load distribution problem between the two arms can be eliminated. With this approach, higher object loads can be manipulated compared to non-optimized methods. Simulations with a 14 degree of freedom (dof) dual-arm robotic system demonstrate the effectiveness of the proposed control method. The real-time feasibility is guaranteed with an average computation time of less than 0.35 milliseconds at a control rate of 1 kilohertz.

This article presents the concept of model predictive interaction control (MPIC) as a generic, flexible, and comprehensive approach for robotic manipulation tasks. MPIC is based on the repetitive solution of an optimal control problem that includes a robot model for motion prediction as well as an interaction model for force prediction. In order to handle both elastic and rigid contact situations, a cascaded approach with low-level PD control is adopted, which allows to combine the linear-elastic environment model and the limited controller stiffness. Due to its flexibility, MPIC can be favorably used for realizing the elementary manipulation primitives (MP) within a hierarchical task planning framework, where each MP corresponds to a particular parameterization of the cost function and the constraints. The control methodology and the manipulation approach are evaluated in simulations and experiments using a 7-degree-of-freedom industrial robot.

The scalability of the modular multilevel converter (MMC) topology renders it suitable for high voltage direct current (HVDC) applications. Thus, this topology plays a significant role in long distance power transmissions, asynchronous interconnections, and long undersea cable crossings. In this paper, a novel model predictive control (MPC) scheme is presented, which performs a circulating current control and energy balancing of an MMC. The proposed MPC formulation unites diverging control objectives specific to optimal steady state operation and hardware protection in case of AC side faults. The control performance is evaluated in simulations for steady state operation and an AC side ground short-circuit scenario. In addition, the MPC scheme is compared to the performance of a standard controller generally used in the industry.

In this article a motion cueing algorithm (MCA) based on model predictive control (MPC) for a hexapod-based dynamic driving simulator is derived. The design objective of the MCA is to reproduce the real world accelerations and angular velocities for a test person in the simulator while respecting its actuator limitations. This results in an underlying nonlinear, state-constrained optimal control problem. In order to exploit the full predictive potential of the described algorithm, a method to predict the future driver behavior and thus the future desired values for the MPC is derived by modeling the driver as an optimal controller. The OCP weights that mainly influence the predicted driving actions are learned from demonstration using an inverse optimal control (IOC) approach. Furthermore, the direct incorporation of human perception models into the motion planning process is considered. An online driver-in-the-loop experiment with the Daimler driving simulator shows the high potential of the derived MPC scheme compared to the commonly used filter-based approach as well as the efficiency of the underlying optimization method.

The development of modern sensitive lightweight robots allows the use of robot arms in numerous new scenarios. Especially in applications where interaction between the robot and an object is desired, e.g. in assembly, conventional purely position-controlled robots fail. Former research has focused, among others, on control methods that center on robot-environment interaction. However, these methods often consider only separate scenarios, as for example a pure force control scenario. The present paper aims to address this drawback and proposes a control framework for robot-environment interaction that allows a wide range of possible interaction types. At the same time, the approach can be used for setpoint generation of position-controlled robot arms, where no interaction takes place. Thus, switching between different controller types for specific interaction kinds is not necessary. This versatility is achieved by a model predictive control-based framework which allows trajectory following control of joint or end-effector position as well as of forces for compliant or rigid robot-environment interactions. For this purpose, the robot motion is predicted by an approximated dynamic model and the force behavior by an interaction model. The characteristics of the approach are discussed on the basis of two scenarios on a lightweight robot.

Motion trajectory planning is one crucial aspect for automated vehicles, as it governs the own future behavior in a dynamically changing environment. A good utilization of a vehicle's characteristics requires the consideration of the nonlinear system dynamics within the optimization problem to be solved. In particular, real-time feasibility is essential for automated driving, in order to account for the fast changing surrounding, e.g. for moving objects. The key contributions of this paper are the presentation of a fast optimization algorithm for trajectory planning including the nonlinear system model. Further, a new concurrent operation scheme for two optimization algorithms is derived and investigated. The proposed algorithm operates in the submillisecond range on a standard PC. As an exemplary scenario, the task of driving along a challenging reference course is demonstrated.

This contribution presents a motion cueing algorithm (MCA) for driving simulators using nonlinear model predictive control (MPC). The goal of the MCA is to generate a realistic motion feeling while keeping the simulator within its workspace limits. The approach relies on a realtime gradient algorithm in combination with the augmented Lagrangian method in order to directly incorporate the system constraints into the optimization. Simulation results for a reference trajectory with typical driving situations demonstrate the performance as well as the computational efficiency of the approach.

This paper addresses the optimization-based planning of collision-free motions for a dual-arm robot with kinematic constraints. Such problems arise, for example, when the robot has to move an object with both arms, whereby the two arms and the gripped object form a closed kinematic chain. Such constrained problems are hard to solve with sampling-based planners, because the probability that a random sample satisfies the closure constraint is practically zero. In contrast, the solution of optimization problems with equality constraints is a well-understood field of research. This paper formulates the motion planning task as optimization problem and proposes a numerical solution using the augmented Lagrangian method for handling constraints. The planner is compared to RRTs, CHOMP and TrajOpt on a set of randomly generated problems for a dual-arm robot with twelve degrees of freedom highlighting the advantages of optimization-based planning.

A control concept is presented for hopping robots with hip and tail actuation. The flight phase is controlled by a novel nonlinear control concept that accounts for state and input contraints on the hip and tail while pursuing a linear error dynamics for the desired landing angle of the leg. An additional nonlinear model predictive control (MPC) scheme is superposed to coordinate the hopping cycles and to maximize the hopping speed. The MPC can be designed with a simple nonlinear optimization algorithm, as the constraints are already accounted for by the cascaded controller. Simulation results for a nonlinear dynamical model of the Festo BionicKangaroo show the working principle of the bi-level predictive control scheme.

We present an analysis of the error growth in inertial tracking due to sensor noise. This analysis focuses on a problem arising in tracking systems with both optical and inertial sensors. Optical sensors always need a line-of-sight, and a natural idea is to continue tracking using only inertial sensors during an occlusion when the line-of-sight is lost. Several error sources are present in inertial tracking; here we consider the error due to sensor noise which cannot be compensated and is present even if the setup is perfectly calibrated and initialized. The result of this analysis is a mathematical expression for the expected error as a function of time and provides an answer to the following two questions: Depending on the precision needed and the inertial sensors employed, for how long is purely inertial tracking possible? Which sensor characteristics have to be improved to decrease the tracking error?

In this paper, a Lagrangian decomposition scheme for the agent based distributed dynamic optimization of coupled nonlinear continuous-time systems is presented. In contrast to existing decomposition schemes, each agent is augmented with approximate dynamics of the coupled neighbor agents, thus enabling the agent to anticipate the dynamic behavior of his neighbors. The performance of the presented decomposition scheme is compared to a standard decomposition scheme by simulation results for a cooperative payload transport by a team of physically coupled robots.