Predominantly, complex optimization techniques are used for path reconstruction given noisy measurements. However, optimization techniques often require the selection of suitable models, tedious parameter tuning and typically fail to generalize to higher-level tasks. In this paper, we present a model-free path filtering method based on the popular moving average method, namely the Curvature Corrected Moving Average (CCMA), which convinces by its simplicity and broad applicability. The moving average is characterized by its unique noise suppression property, albeit curves are bent inwards, which adversely affects its accuracy. By utilizing the relation between both curvatures, the original curvature can be inferred based on the curvature of filtered points. Extending the symmetric filtering not only succeeds in minimizing noise but retains the original shape of the path, making it a suitable algorithm for a variety of robotic applications. We demonstrate the practicality of the approach in a real-world convoy scenario: The accumulated estimates of the leader vehicle’s position, originating from an Extended Kalman filter, are smoothed using our novel approach to generate proper inputs for the Model Predictive Controller (MPC). This cascade structure of filtering provides both responsiveness and smoothness. Furthermore, we successfully applied this method in the off-road convoy scenario for the ELROB 2022, where we won first place. The source code is publicly available.

In physical human-robot interaction (pHRI), safety is a key requirement. As collisions between humans and robots can generally not be avoided, it must be ensured that the human is not harmed. The robot reflected mass, the contact geometry, and the relative velocity between human and robot are the parameters that have the most significant influence on human injury severity during a collision. The reflected mass depends on the robot configuration and can be optimized especially in kinematically redundant robots. In this paper, we propose the Mean Reflected Mass (MRM) metric. The MRM is independent of the direction of contact/motion and enables assessing and optimizing the robot posture w.r.t. safety. In contrast to existing metrics, it is physically interpretable, meaning that it can be related to biomechanical injury data for realistic and model-independent safety analysis. For the Franka Emika Panda, we demonstrate in simulation that an optimization of the robot's MRM reduces the mean collision force. Finally, the relevance of the MRM for real pHRI applications is confirmed through a collision experiment.