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

Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

571
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...
571
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

401
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...
401
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.1K
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...
4.1K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

666
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...
666
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

323
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
323
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

487
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...
487

You might also read

Related Articles

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

Sort by
Same author

Associations of sarcopenia with the risk of incident respiratory disease and the role of inflammation and metabolism: a prospective cohort study.

The journal of nutrition, health & aging·2026
Same author

Starmate: A Lightweight AI Assistant for Autism Caregivers Developed and Evaluated Through a User-Centered Mixed-Methods Framework.

Journal of medical systems·2026
Same author

Efficacy of fully automated digital cognitive behavioral therapy for insomnia in adults: a systematic review and meta-analysis.

Sleep & breathing = Schlaf & Atmung·2026
Same author

The clinical value of pharmacogenomics in the pharmacotherapy of common psychiatric disorders: a macroscopic analysis based on real-world data.

BMC psychiatry·2026
Same author

Association of Multiple Obesity-Related Composite Indices with All-Cause Mortality in Patients with Stage 0-3 Cardiovascular-Kidney-Metabolic Syndrome.

Endocrinology and metabolism (Seoul, Korea)·2026
Same author

Auditing the impact of social media's policy shift on anti-vaccine discourse: A large language model-driven empirical study.

PloS one·2026

Related Experiment Video

Updated: Jun 27, 2025

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

11.6K

Robot obstacle avoidance optimization by A* and DWA fusion algorithm.

Peiying Li1, Lingjuan Hao1, Yanjie Zhao1

  • 1Mechanical and Electrical College, Handan University, Handan, 056005, China.

Plos One
|April 29, 2024
PubMed
Summary

This study introduces a hybrid robot path planning algorithm combining improved A-star global planning and fuzzy-controlled sliding window local planning. The novel approach enhances dynamic obstacle avoidance and efficiency for mobile robots.

More Related Videos

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.6K
Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
09:00

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Published on: December 19, 2016

14.6K

Related Experiment Videos

Last Updated: Jun 27, 2025

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

11.6K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.6K
Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
09:00

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Published on: December 19, 2016

14.6K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Current robot path planning methods often struggle with real-time requirements and dynamic obstacle avoidance.
  • Existing global or local planning approaches lack the integration needed for complex, dynamic environments.

Purpose of the Study:

  • To develop a hybrid robot obstacle avoidance path planning algorithm.
  • To improve upon traditional A-star and sliding window methods for enhanced performance.

Main Methods:

  • Optimized the A-star algorithm by refining its evaluation function, sub-node selection, and path smoothing.
  • Integrated fuzzy control to enhance the sliding window local planning method.
  • Validated the hybrid algorithm on a TurtleBot3 mobile robot using experimental data.

Main Results:

  • The hybrid algorithm successfully navigated dynamic obstacles and reached target points accurately.
  • Demonstrated a 9.6% reduction in path length and a 29% decrease in planning time compared to traditional methods.
  • Achieved an approximate 26.7% increase in the robot's average speed.

Conclusions:

  • The proposed hybrid algorithm significantly improves dynamic obstacle avoidance, planning efficiency, and model adaptability.
  • Offers a valuable reference for robot path planning and obstacle avoidance optimization in practical applications.