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

Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

702
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...
702
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
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...
4.3K
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

629
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...
629
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

428
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...
428
Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

482
Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
482
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

12.5K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
12.5K

You might also read

Related Articles

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

Sort by
Same author

Adapting virtual agent interaction style with reinforcement learning to enhance affective engagement.

Frontiers in digital health·2026
Same author

Automated planning and scheduling in robot-aided rehabilitation: a review.

Journal of neuroengineering and rehabilitation·2025
Same author

Exploring Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study.

JMIR aging·2025
Same author

Teaching control courses online during the covid-19 pandemic: some experiences at the University of Brescia.

IFAC-PapersOnLine·2024
Same author

A dichotomic approach to adaptive interaction for socially assistive robots.

User modeling and user-adapted interaction·2022
Same author

A Mind-inspired Architecture for Adaptive HRI.

International journal of social robotics·2022

Related Experiment Video

Updated: Aug 2, 2025

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.7K

Optimal Task and Motion Planning and Execution for Multiagent Systems in Dynamic Environments.

Marco Faroni, Alessandro Umbrico, Manuel Beschi

    IEEE Transactions on Cybernetics
    |April 13, 2023
    PubMed
    Summary

    This study introduces a novel approach for multiagent systems, combining task and motion planning to handle variable task durations and spatial constraints. The method optimizes task sequencing and execution, significantly reducing overall process time in collaborative scenarios.

    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

    6.0K
    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

    12.7K

    Related Experiment Videos

    Last Updated: Aug 2, 2025

    Operation of the Collaborative Composite Manufacturing CCM System
    10:09

    Operation of the Collaborative Composite Manufacturing CCM System

    Published on: October 1, 2019

    6.7K
    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

    6.0K
    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

    12.7K

    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Operations Research

    Background:

    • Multiagent systems face challenges in integrating symbolic reasoning with geometric constraints for planning, scheduling, and synchronization.
    • Previous research often neglects the inherent variability in task duration and geometric feasibility due to agent-environment interactions.

    Purpose of the Study:

    • To develop a combined task and motion planning framework that optimizes task sequencing, assignment, and execution under temporal and spatial variability.
    • To address the limitations of existing works by incorporating dynamic environmental changes and agent interactions.

    Main Methods:

    • Decoupling symbolic tasks from their geometric action realizations.
    • Utilizing timeline-based planning for task-level temporal constraints, duration variability, and synergistic task assignment.
    • Employing online motion planning for action-level movement generation that adapts to environmental changes.

    Main Results:

    • Demonstrated effectiveness in a collaborative manufacturing scenario involving a robotic arm and a human worker assembling a mosaic.
    • Achieved significant reduction in overall process execution time compared to existing methods.
    • Showcased applicability to a broader range of multiagent planning and execution problems.

    Conclusions:

    • The proposed combined task and motion planning approach effectively manages temporal and spatial variability in multiagent systems.
    • This framework enhances efficiency and reduces execution time in complex collaborative tasks.
    • The decoupling strategy provides a flexible and robust solution for dynamic environments.