Jove
Visualize
Contact Us

Related Concept Videos

Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

618
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...
618
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.1K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
5.1K
Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

299
To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
299
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

545
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...
545
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.2K
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...
1.2K
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

1.1K
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...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Genetic algorithm-based coverage path planning for autonomous aircraft cabin cleaning by reconfigurable robot.

PloS one·2026
Same author

Determining optimum assembly zone for modular reconfigurable robots using multi-objective genetic algorithm.

Scientific reports·2025
Same author

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging.

Journal of imaging·2024
Same author

Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation.

Sensors (Basel, Switzerland)·2023
Same author

Patient-Specific Finite Element Modeling of the Whole Lumbar Spine Using Clinical Routine Multi-Detector Computed Tomography (MDCT) Data-A Pilot Study.

Biomedicines·2022
Same author

Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications.

Sensors (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
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: Nov 18, 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

12.0K

Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot.

Koppaka Ganesh Sai Apuroop1, Anh Vu Le2, Mohan Rajesh Elara1

  • 1ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore.

Sensors (Basel, Switzerland)
|February 9, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep reinforcement learning framework for tiling robots to achieve complete area coverage efficiently. The novel approach optimizes robot shape and path planning, minimizing energy consumption and reconfigurations for enhanced cleaning performance.

Keywords:
complete coverage planingenergy path planningreconfigurable robotreinforcement learningtiling robots

More Related Videos

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
13:44

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

14.3K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.9K

Related Experiment Videos

Last Updated: Nov 18, 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

12.0K
Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
13:44

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

14.3K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.9K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Commercial cleaning robots have limitations in area coverage due to fixed morphology.
  • Tiling robots offer a solution for complete area coverage in diverse applications like cleaning and inspection.
  • Optimizing area coverage while minimizing energy consumption and reconfigurations is crucial for tiling robots.

Purpose of the Study:

  • To propose a complete area coverage planning module for a honeycomb-shaped tiling robot (hTrihex).
  • To develop a framework using deep reinforcement learning for simultaneous shape and trajectory generation.
  • To minimize the overall cost, including energy consumption and reconfigurations.

Main Methods:

  • A deep reinforcement learning approach was employed, specifically training a convolutional neural network (CNN) with a long short-term memory (LSTM) layer.
  • The actor-critic experience replay (ACER) algorithm was utilized for training the neural network.
  • The proposed method was compared against traditional tiling theories (zigzag, spiral, greedy search) and optimization approaches (genetic algorithm, ant colony optimization for TSP).

Main Results:

  • The proposed deep reinforcement learning framework successfully generates tiling shapes and trajectories.
  • The method achieves minimized overall cost, indicating efficient area coverage.
  • The approach resulted in a minimized cost path generated in a lesser time compared to traditional and optimization-based methods.

Conclusions:

  • The deep reinforcement learning-based coverage planning module enhances the efficiency of tiling robots.
  • The framework effectively balances maximizing area coverage with minimizing energy consumption and reconfigurations.
  • This approach offers a superior alternative to existing methods for complete area coverage planning in robotics.