Motion simulation can substantially improve the immersion of any form of vehicle simulation. However, inadequate motion simulation can fully break the immersion or even induce adverse effects, such as discomfort, motion sickness, or other harm to the simulator occupants. The selection of a stable and safe motion cueing algorithm (MCA) is therefore essential. In particular, complex and simultaneously real-time capable MCAs can carry the risk of instability. This phenomenon can be observed in non-linear model predictive control-based MCAs when the prediction horizon is defined too short, and in learning-based MCAs when there is insufficient utilization of training data. Specifically, the employment of artificial neural networks in the modeling of MCAs can lead to problems such as lack of generalization or overfitting, which, combined with the difficult interpretability due to the black-box character, makes analytical guarantees difficult. The problem is further intensified when the MCA is in a control loop with a motion platform that has a highly non-linear behavior.This work proposes a sample-based framework that utilizes simulative modeling of the deployed simulator platform to investigate the behavior of MCAs. The proposed framework is structured to initialize the system in random states, which allows for a comprehensive investigation of the behavior of any non-specific MCA. The framework is applied to two variations of one state-of-the-art MCA, and the results are compared. It is shown that the framework can identify deficiencies in the trajectory planning of MCAs. Thus, it is able to contribute significantly to the safety and stability of motion simulation.

MoRC is a high-performance modular robot controller based on the Functional Mock-up Interface (FMI) standard. The goal is to control any (industrial) robot with electrical drives using a customizable vendor-agnostic control cabinet and an innovative, self-developed software architecture based on exchangeable multi-rate real-time control components with standardized interfaces. On the hardware side, the use of EtherCAT (Ethernet for Control Automation Technology) allows connecting a freely selectable number of COTS (commercial off-the-shelf) electrical drives and sensors. On the software side, this is matched with exchangeable control software modules based on the FMI standard. Those can be interconnected for forming user-defined multi-rate control structures which can be executed as synchronized real-time threads on a central Linux-based multi-core computing unit. That unlocks additional computational potential for advanced high-frequency control algorithms. Control structures can be switched at runtime to handle highly diverse control tasks. This paper presents the architectural concepts as well as first experiments on an industrial robot testbed.

Digital human models (DHM) offer a great possibility of design evaluation for products humans interact with. One field to apply DMH’s is the dynamic analysis of human-machine interaction. Thereby, the dynamics of the human body can significantly influence the entire system behavior and thus it is important to consider during the design process. For the modeling language Modelica no simulation library for this application is available, so far. The language is especially suitable for the simulation across different domains, which makes a DHM in Modelica useful. In this paper a DHM predicting the dynamic behavior of the human body during human-machine interaction is implemented in Modelica. It consists of a multi-body skeleton model, an inverse kinematics algorithm for the limbs and a controller for the skeleton joints inspired by human motor control. The capability of the proposed simulation approach to take the relevant dynamical properties of the human body and motor control into account is demonstrated in a use-case simulation. As use-case the aircraft-pilot coupling during a strong gust acting on a sports aircraft is investigated.

Parallel manipulators are recently used in most motion simulation laboratories as they can easily generate six degrees of freedom motion. Recently, fuzzy logic-based adaptive motion cueing algorithms (MCAs) have been employed to reproduce the motion signals. The usage of fuzzy logic-based adaptive MCA reduces the movement sensation error between the real vehicle and the simulation-based motion platform (SBMP) user considering the end-effector limitations in Cartesian space.. In this paper, a new fuzzy logic-based adaptive MCA is introduced to generate motion signals based on the joints' limitations and the movement sensation error between the real vehicle and the SBMP user. Considering the parallel SBMP, joint limits enhance the ability of the introduced adaptive motion cueing algorithm to generate more accurate movement feelings with high fidelity. The simulation results prove that the proposed adaptive motion cueing algorithm can effectively use the large workspace of the parallel SBMP whilst reducing the motion sensation error.

Motion simulators based on industrial robots can produce high dynamic accelerations and velocities compared to classical hydraulic hexapod systems. In case of emergency stops, large and possibly harmful accelerations can occur. This paper aims to provide an optimization procedure to generate worst case trajectories in order to test for these harmful accelerations, by maximizing the kinetic energy prior the emergency stop. The dynamical and mechanical limits of the robot are considered as constraints of the optimization criterion. An exemplary worst case trajectory is simulated using a braking model and the resulting Head Injury Criterion (HIC) is calculated and compared with older tests, using non-optimized trajectories. A significant higher, yet with the current robot dynamics not harmful HIC value can be generated.

The DLR Robot Motion Simulator is a serial kinematics based platform that employs an industrial robot (as opposed to the conventional `Hexapod') to impart motion cues to the attached simulator cell. This simulation platform is the culmination of ongoing research on motion simulation at the Robotics and Mechatronics Center, German Aerospace Center (DLR). Safety tests were undertaken to ascertain the effects of critical motions and subsequent emergency stop procedures on the prospective human passengers of the simulator cell. To this end, an Anthropomorphic Test Device (ATD) aka `crash test dummy' was used as a human surrogate for these tests. Several severity indices were evaluated for the head-neck region, which was found to be more susceptible to injuries compared to the rest of the body. The results of this study are discussed in this paper.

In recent years a new generation of motion simulators, based on serial kinematics industrial robots, emerged as alternative to the currently prevalent Steward-platforms. This paper presents the newest addition to this: The DLR Robot Motion Simulator. Part I covers the design process and gives a detailed introduction of the setup. The overall layout of the simulation platform and its parts is introduced. To meet the requirements of an interactive simulation, a new piloting cell had to be designed using light-weight construction and real time vehicle simulations were required. Another key issue of the whole design is the guarantee of safety. The simulator utilizes a 10m linear axis to increase its workspace to provide a better simulation experience compared to previous designs of motion simulators with serial or parallel kinematics. Part II introduces a new path planning algorithm for the kinematically redundant simulator platform, required for the generation of appropriate motion cues. Furthermore the application artistic-flight simulation is demonstrated and validated.

In Part I of this paper, a novel motion simulator platform is presented, the DLR Robot Motion Simulator with 7 degrees of freedom (DOF). In this Part II, a path-planning algorithm for mentioned platform will be discussed. By replacing the widely used hexapod kinematics by an antropomorhic, industrial robot arm mounted on a standard linear axis, a comparably larger workspace at lower hardware costs can be achieved. But the serial, redundant kinematics of the industrial robot system also introduces challenges for the path-planning as singularities in the workspace, varying movability of the system and the handling of robot system's kinematical redundancy. By solving an optimization problem with constraints in every sampling step, a feasible trajectory can be generated, fulfilling the task of motion cueing, while respecting the robot's dynamic constraints.