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Crossing-Point Estimation in Human-Robot Navigation-Statistical Linearization versus Sigma-Point Transformation.

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  • 1Center for Applied Autonomous Sensor Systems (AASS), Department of Technology, Örebro University, SE-701 82 Örebro, Sweden.

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Summary
This summary is machine-generated.

This study addresses safety in human-robot navigation by calculating expected interaction areas. It uses statistical linearization and sigma-point transformation to handle fuzzy and noisy trajectory data for safer robot navigation.

Keywords:
Gaussian noisehuman–robot interactionsigma-point transformationunscented Kalman filter

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Area of Science:

  • Robotics
  • Human-Robot Interaction
  • Navigation Systems

Background:

  • Safe navigation in shared spaces is critical for mobile robots and human operators.
  • Trajectory planning and obstacle avoidance are key safety considerations.
  • Uncertainty in planned trajectories due to noise impacts safety.

Purpose of the Study:

  • To develop methods for calculating expected interaction areas between humans and robots.
  • To address the challenges of fuzzy and noisy information in human-robot navigation.
  • To analyze the nonlinear association between robot/human states and trajectory intersection areas.

Main Methods:

  • Statistical linearization for nonlinear transformation of noisy inputs.
  • Sigma-point transformation for uncertainty propagation.
  • Fuzzy approximations for both methods.
  • Discussion of the inverse problem for parameter estimation.

Main Results:

  • Quantification of expected interaction areas considering trajectory uncertainties.
  • Demonstration of nonlinear mapping from motion inputs to intersection areas.
  • Presentation of fuzzy approximations for practical application.

Conclusions:

  • The proposed methods effectively calculate expected interaction areas in human-robot navigation.
  • Handling fuzzy and noisy data is essential for robust trajectory planning.
  • Understanding these interaction areas enhances safety in shared environments.