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

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.9K
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.
5.9K
Direct Motor Pathways01:11

Direct Motor Pathways

4.1K
The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
The corticospinal tract is responsible for the voluntary movement of the limbs and trunk. It originates in the cerebral cortex of the brain and descends through the cerebrum's internal capsule and...
4.1K

You might also read

Related Articles

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

Sort by
Same author

ROTEM versus conventional coagulation tests in evaluating coagulopathy and transfusion requirement in ECMO patients: A retrospective study.

Perfusion·2026
Same author

Antioxidant biomaterials in intervertebral disc regeneration: current status and future clinical translation.

Frontiers in bioengineering and biotechnology·2026
Same author

Human-Risk-Aware Safe Path Planning Based on Reinforcement Learning for Autonomous Mobile Robots.

Sensors (Basel, Switzerland)·2025
Same author

Risk prediction of osteoporotic vertebral compression fractures in postmenopausal osteoporotic women by machine learning modelling.

Frontiers in medicine·2025
Same author

A comparative study of the efficacy of whether or not to preserve the joint capsule during PLIF surgery in patients with isthmic spondylolisthesis.

Frontiers in surgery·2025
Same author

A mobile robot safe planner for multiple tasks in human-shared environments.

PloS one·2025
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

Related Experiment Video

Updated: Jan 10, 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

12.1K

A Multi-Policy Rapidly-Exploring Random Tree Based Mobile Robot Controller for Safe Path Planning in Human-Shared

Jian Mi1, Xianbo Zhang2, Zhongjie Long2

  • 1Department of Transport Engineering, School of Civil Engineering and Transportation, Yangzhou University, Yangzhou 225127, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-policy rapidly exploring random tree (MP-RRT) algorithm for mobile robot path planning in environments with unpredictable human movement. The MP-RRT ensures robot safety by optimizing paths based on human motion uncertainties, outperforming existing methods.

Keywords:
human-shared environmentsmobile robotmulti-policy rapidly exploring random tree searchsafe path planningstochastic risk evaluation

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

8.5K
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

15.1K

Related Experiment Videos

Last Updated: Jan 10, 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

12.1K
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

8.5K
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

15.1K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Mobile robot path planning in static environments is well-researched.
  • Ensuring safety in dynamic environments with stochastic human motion presents a significant challenge for mobile robots.
  • Existing algorithms struggle with unpredictable human behavior, leading to potential conflicts.

Purpose of the Study:

  • To develop a safe pathfinding algorithm for mobile robots operating in human-shared environments with unknown human motions.
  • To address the challenge of preventing conflicts at the planning level by modeling human motion uncertainties.
  • To generate an optimally safe path for robots collaborating with humans.

Main Methods:

  • Proposed a multi-policy rapidly exploring random tree (MP-RRT)-based safe pathfinding algorithm.
  • Developed a MP-RRT diverse path generator to create multiple candidate paths.
  • Introduced a dynamic quadrant-based stochastic exploration mechanism for efficient environment mapping.
  • Designed a path optimization mechanism using stochastic risk evaluation to model human motion uncertainties.

Main Results:

  • The MP-RRT algorithm significantly reduced conflict numbers compared to A*, MDP, and RRT (-70.2%, -72.8%, and -73.8%, respectively).
  • Achieved substantial improvements in task success rate (+66.0%, +95.0%, and +85.7% over A*, MDP, and RRT).
  • Demonstrated efficient environment exploration and optimal safe path generation by considering human risks.

Conclusions:

  • The proposed MP-RRT algorithm effectively ensures robot safety in human-shared environments by explicitly modeling human motion uncertainties.
  • The algorithm outperforms traditional methods in reducing conflicts and increasing task success rates.
  • Simulation results validate the efficiency and robustness of the proposed approach for safe path planning in dynamic, human-populated spaces.