Version 1
: Received: 27 March 2024 / Approved: 28 March 2024 / Online: 28 March 2024 (15:26:29 CET)
How to cite:
Palm, R.H.; Lilienthal, A.J. Crossing Point Estimation in Human/Robot Navigation -
Statistical Linearization versus Sigma-Point-Transformation. Preprints2024, 2024031747. https://doi.org/10.20944/preprints202403.1747.v1
Palm, R.H.; Lilienthal, A.J. Crossing Point Estimation in Human/Robot Navigation -
Statistical Linearization versus Sigma-Point-Transformation. Preprints 2024, 2024031747. https://doi.org/10.20944/preprints202403.1747.v1
Palm, R.H.; Lilienthal, A.J. Crossing Point Estimation in Human/Robot Navigation -
Statistical Linearization versus Sigma-Point-Transformation. Preprints2024, 2024031747. https://doi.org/10.20944/preprints202403.1747.v1
APA Style
Palm, R.H., & Lilienthal, A.J. (2024). Crossing Point Estimation in Human/Robot Navigation -
Statistical Linearization versus Sigma-Point-Transformation. Preprints. https://doi.org/10.20944/preprints202403.1747.v1
Chicago/Turabian Style
Palm, R.H. and Achim J. Lilienthal. 2024 "Crossing Point Estimation in Human/Robot Navigation -
Statistical Linearization versus Sigma-Point-Transformation" Preprints. https://doi.org/10.20944/preprints202403.1747.v1
Abstract
Interactions between mobile robots and human operators in common areas require a high safety especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection the crossings of planned trajectories, their uncertainty based on model fluctuations, system noise and sensor noise,play an outstanding role. This paper discusses the calculation of expected areas of interactions du ring human-robot navigation with respect to fuzzy and noisy information. Expected crossing points of possible trajectories are nonlinearily associated with positions and orientations of robot and human. The nonlinear transformation of a noisy system input, such as directions of motion of human and robot, to a system output, the expected area of intersection of
their trajectories, is done by two methods: statistical linearization and the sigma-point-transformation. For both approaches fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from given output distribution parameters.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
