Jove
Visualize
Contact Us
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 Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.6K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

802
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
802
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

879
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
879
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.4K
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
1.4K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.2K
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

1.4K
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Correction: ROS 4 healthcare: a framework for physiological human sensing for social, assistive, rehabilitation, and medical robotics.

Frontiers in robotics and AI·2026
Same author

ROS 4 healthcare: a framework for physiological human sensing for social, assistive, rehabilitation, and medical robotics.

Frontiers in robotics and AI·2026
Same author

Conceptual design of the energy-switchable storage ring as a high-brilliance light source over a wide wavelength range.

Journal of synchrotron radiation·2025
Same author

Somnomat Care: A Novel Robotic Bed for Vestibular Stimulation in Nursing Homes.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

Conceptual design of the Hybrid Ring with superconducting linac.

Journal of synchrotron radiation·2022
Same author

ITC: Infused Tangential Curves for Smooth 2D and 3D Navigation of Mobile Robots <sup>†</sup>.

Sensors (Basel, Switzerland)·2019

Related Experiment Video

Updated: Feb 27, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.3K

Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing.

Abhijeet Ravankar1, Ankit A Ravankar2, Yukinori Kobayashi3

  • 1Faculty of Engineering, Lab of Robotics and Dynamics, Hokkaido University, Sapporo 060-8628, Japan. abhijeet@frontier.hokudai.ac.jp.

Sensors (Basel, Switzerland)
|July 6, 2017
PubMed
Summary

Multiple service robots can share map changes like path blockages to efficiently update remote areas and plan better routes. This knowledge sharing improves navigation without robots needing to visit every location.

Keywords:
robot path planning, multi-robot knowledge sharing, robots in sensor network

More Related Videos

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents
04:41

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Published on: December 2, 2022

3.4K

Related Experiment Videos

Last Updated: Feb 27, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.3K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents
04:41

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Published on: December 2, 2022

3.4K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Multi-agent Systems

Background:

  • Large-scale operations often employ multiple service robots, each managing its own environmental map.
  • Current systems lack efficient mechanisms for robots to share real-time map updates, leading to potential navigation inefficiencies.

Purpose of the Study:

  • To propose and evaluate a novel knowledge-sharing mechanism for multi-robot systems.
  • To enable robots to collaboratively update environmental maps and enhance path planning efficiency.

Main Methods:

  • A symbiotic information-sharing framework allowing robots to communicate map changes (e.g., path blockages, new obstacles).
  • Utilizing a node representation for seamless sharing of blocked path information.
  • Modeling obstacle transience to track dynamic changes in the environment.
  • Implementing a lazy information update scheme for task-relevant updates.

Main Results:

  • The proposed method allows robots to update remote map areas without direct navigation.
  • Demonstrated improved path planning efficiency compared to traditional methods.
  • Experimental validation in both simulated and real-world environments confirmed the system's effectiveness.

Conclusions:

  • The developed knowledge-sharing mechanism significantly enhances multi-robot coordination and navigation.
  • Enables more efficient and adaptive path planning in dynamic, large-scale operational areas.
  • Facilitates collaborative environmental mapping and obstacle management for service robots.