Interactions between mobile robots and human operators in common areas require a high level of safety, especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection, the crossings of planned trajectories and their uncertainty based on model fluctuations, system noise and sensor noise play an outstanding role. This paper discusses the calculation of the expected areas of interactions during human-robot navigation with respect to fuzzy and noisy information. The expected crossing points of the possible trajectories are nonlinearly associated with the positions and orientations of the robots and humans. The nonlinear transformation of a noisy system input, such as the directions of the motion of humans and robots, to a system output, the expected area of intersection of their trajectories, is performed 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 the given output distribution parameters.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11175143 | PMC |
| http://dx.doi.org/10.3390/s24113303 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Crossing-Point Estimation in Human-Robot Navigation-Statistical Linearization versus Sigma-Point Transformation.
Sensors (Basel)
May 2024
Technical University Munich (TUM), 80333 Munich, Germany.
Interactions between mobile robots and human operators in common areas require a high level of safety, especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection, the crossings of planned trajectories and their uncertainty based on model fluctuations, system noise and sensor noise play an outstanding role. This paper discusses the calculation of the expected areas of interactions during human-robot navigation with respect to fuzzy and noisy information.
View Article and Find Full Text PDFUncertainty analysis of vehicle-pedestrian accident reconstruction based on unscented transformation.
Forensic Sci Int
January 2023
Yunnan Yuntong Judicial Expertise Center, Kunming 650255, China.
In order to investigate the sensitivity of parameters and analyze the uncertainty of reconstructed results in traffic accident, the impact of correlations between parameters on accident reconstruction results was taken into account using uncertainty analysis. Based on unscented transformation (UT), a parameter sensitivity analysis method and an efficient uncertainty analysis method in accident reconstruction were proposed. Sensitivity analysis was performed through the sigma point sets generated by the UT method.
View Article and Find Full Text PDFAn Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking.
Sensors (Basel)
November 2020
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection.
View Article and Find Full Text PDFRobust cubature Kalman filter based on variational Bayesian and transformed posterior sigma points error.
ISA Trans
March 2019
Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
An improved robust cubature Kalman filter (RCKF) based on variational Bayesian (VB) and transformed posterior sigma points error is proposed in this paper, which not only retains the robustness of RCKF, but also exhibits adaptivity in the presence of time-varying noise. First, a novel sigma-point update framework with uncertainties reduction is developed by employing the transformed posterior sigma points error. Then the VB is used to estimate the time-varying measurement noise, where the state-dependent noise is addressed in the iteratively parameter estimation.
View Article and Find Full Text PDFIndoor Pedestrian Localization Using iBeacon and Improved Kalman Filter.
Sensors (Basel)
May 2018
School of Electrical Engineering, Korea University, Seoul 02841, Korea.
The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user's motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!