Jove
Visualize
Contact Us

Related Concept Videos

Survival Tree01:19

Survival Tree

115
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
115
Random Sampling Method01:09

Random Sampling Method

11.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.2K
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

455
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
455
Randomized Experiments01:13

Randomized Experiments

7.0K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.0K
Machines: Problem Solving II01:30

Machines: Problem Solving II

336
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
336
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
81

You might also read

Related Articles

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

Sort by
Same author

Integrating Heuristic Methods with Deep Reinforcement Learning for Online 3D Bin-Packing Optimization.

Sensors (Basel, Switzerland)·2024
Same author

A Robotics Experimental Design Method Based on PDCA: A Case Study of Wall-Following Robots.

Sensors (Basel, Switzerland)·2024
Same author

Multi-Sensor Fusion Simultaneous Localization Mapping Based on Deep Reinforcement Learning and Multi-Model Adaptive Estimation.

Sensors (Basel, Switzerland)·2024
Same author

Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection.

Sensors (Basel, Switzerland)·2021
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: Jul 23, 2025

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

241

Implementation of a Real-Time Object Pick-and-Place System Based on a Changing Strategy for Rapidly-Exploring Random

Ching-Chang Wong1, Chong-Jia Chen1, Kai-Yi Wong2

  • 1Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 25137, Taiwan.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
Summary
This summary is machine-generated.

A new Changing Strategy Rapidly-exploring Random Tree (CS-RRT) algorithm enhances robot pick-and-place tasks. This method improves path planning success rates and reduces computation time for autonomous robots in complex environments.

Keywords:
collision-freeobject pick-and-placepath planningrapidly-exploring random tree (RRT)robot manipulatorrobot operating system (ROS)

More Related Videos

Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

8.0K
A Within-Subject Experimental Design using an Object Location Task in Rats
09:28

A Within-Subject Experimental Design using an Object Location Task in Rats

Published on: May 6, 2021

4.6K

Related Experiment Videos

Last Updated: Jul 23, 2025

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

241
Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

8.0K
A Within-Subject Experimental Design using an Object Location Task in Rats
09:28

A Within-Subject Experimental Design using an Object Location Task in Rats

Published on: May 6, 2021

4.6K

Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Computer Science

Background:

  • Autonomous object pick-and-place systems require robust collision-free path planning for robot manipulators.
  • Existing path planning algorithms face challenges in balancing success rate and computation time in complex environments.
  • Six-degree-of-freedom (DOF) robot manipulators are crucial for versatile pick-and-place operations.

Purpose of the Study:

  • To propose an improved path planning algorithm, Changing Strategy Rapidly-exploring Random Tree (CS-RRT), for robot pick-and-place systems.
  • To enhance the success rate and reduce the computing time of collision-free path planning for six-DOF robot manipulators.
  • To validate the effectiveness of the CS-RRT algorithm in complex environments through simulations and practical experiments.

Main Methods:

  • Implementation of an object pick-and-place system using Robot Operating System (ROS), a camera, a six-DOF robot manipulator, and a two-finger gripper.
  • Development of the CS-RRT algorithm, an enhancement of the Gradually Changing Sampling Area Rapidly-exploring Random Tree (CSA-RRT).
  • Incorporation of a sampling-radius limitation mechanism and a node counting mechanism into the CS-RRT algorithm to optimize path planning.

Main Results:

  • The CS-RRT algorithm demonstrated superior performance in terms of success rate and reduced computing time compared to two other RRT algorithms in simulations.
  • The sampling-radius limitation mechanism efficiently guides the random tree towards the goal, minimizing time spent searching near the target.
  • The node counting mechanism allows the algorithm to adapt sampling strategies in complex environments, preventing search path entrapment and improving adaptability.

Conclusions:

  • The proposed CS-RRT algorithm provides an effective solution for collision-free path planning in robot pick-and-place tasks.
  • The CS-RRT algorithm successfully enhances both the efficiency and reliability of robot manipulation in complex, real-world scenarios.
  • Practical experiments confirmed the robot manipulator's ability to complete pick-and-place tasks effectively using the CS-RRT-based path planning.