Jove
Visualize
Contact Us

Related Concept Videos

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

71
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
71
Root-Locus Method01:19

Root-Locus Method

145
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
145
Adjusting a Traverse01:12

Adjusting a Traverse

56
In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
56

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

High-Resolution Wind Tunnel Dataset of Gas Sensor Responses to Vapor Plumes in Scale Model Landscapes.

Scientific data·2026
Same author

Evaluating LiDAR Perception Algorithms for All-Weather Autonomy.

Sensors (Basel, Switzerland)·2025
Same author

Video WeAther RecoGnition (VARG): An Intensity-Labeled Video Weather Recognition Dataset.

Journal of imaging·2024
Same author

Exploration and Gas Source Localization in Advection-Diffusion Processes with Potential-Field-Controlled Robotic Swarms.

Sensors (Basel, Switzerland)·2023
Same author

Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors.

Sensors (Basel, Switzerland)·2023
Same author

Benchmarking the utility of maps of dynamics for human-aware motion planning.

Frontiers in robotics and AI·2022
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 23, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.0K

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

Rainer Palm1, Achim J Lilienthal2

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

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
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

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

12.6K

Related Experiment Videos

Last Updated: Jun 23, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.0K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

12.6K

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.