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

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

559
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
559
PD Controller: Design01:26

PD Controller: Design

233
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
233
Controller Configurations01:22

Controller Configurations

99
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
99

You might also read

Related Articles

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

Sort by
Same author

Distributed Security and Safety-Critical Formation Control for Multirobot Systems Subject to Distributed Denial-of-Service Attacks.

IEEE transactions on cybernetics·2026
Same author

Application of UAV photogrammetry technology in identifying discontinuities in slopes in the Pulang copper mine.

Scientific reports·2026
Same author

A Stochastic Hybrid Approach to Decentralized Networked Control: Stochastic Network Delays and Poisson Pulsing Attacks.

IEEE transactions on cybernetics·2026
Same author

Reduced <i>MdFLS</i> Expression Decreases Flavonol Content in Apple Flesh.

Plants (Basel, Switzerland)·2026
Same author

Peginterferon α-2b Enhances Hepatitis B Surface Antigen Loss in Nucleos(t)ide Analogue-Suppressed Low Hepatitis B Surface Antigen Chronic Hepatitis B Patients: Everest Study in China.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026
Same author

Active disturbance rejection control synthesis for industrial time-delayed process: an observation reconfiguration perspective.

ISA transactions·2026

Related Experiment Video

Updated: Jul 4, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

Deep Reinforcement Learning for Autonomous Driving with an Auxiliary Actor Discriminator.

Qiming Gao1, Fangle Chang1,2, Jiahong Yang1,3

  • 1Ningbo Innovation Center, Zhejiang University, Ningbo 315100, China.

Sensors (Basel, Switzerland)
|January 26, 2024
PubMed
Summary

This study introduces a novel robot navigation system using a state attention network (SAN) and auxiliary actor discriminator (AAD) for efficient path planning and obstacle avoidance in dynamic environments.

Keywords:
autonomous drivingauxiliary actor discriminator (AAD)heuristic knowledge (HK)reinforcement learning (RL)

More Related Videos

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K
The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
10:39

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

8.5K

Related Experiment Videos

Last Updated: Jul 4, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K
The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
10:39

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

8.5K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Path planning and obstacle avoidance are critical for intelligent robots, particularly in unknown dynamic environments.
  • Existing methods often struggle with the flexibility and rapid decision-making required in such complex scenarios.

Purpose of the Study:

  • To develop an advanced robot navigation system capable of effective path planning and obstacle avoidance.
  • To enhance robot decision-making and exploration capabilities in unknown dynamic environments.

Main Methods:

  • A state attention network (SAN) was developed for feature extraction of robot-obstacle interactions.
  • An auxiliary actor discriminator (AAD) was implemented to calculate collision probabilities.
  • Goal-directed and gap-based navigation strategies, guided by heuristic knowledge (HK), were employed.
  • The Soft Actor-Critic (SAC) algorithm was used for policy training in simulated environments.

Main Results:

  • The proposed approach demonstrated convergence towards optimal action strategies for robot systems.
  • Robots explored unknown environments with significantly fewer moving steps, showing a decrease of 33.9%.
  • The system achieved higher average rewards, with an increase of 29.15% compared to other methods.

Conclusions:

  • The developed navigation system enhances robot performance in complex, unknown dynamic environments.
  • The integration of SAN, AAD, and heuristic knowledge offers a robust solution for intelligent robot exploration.
  • This research contributes to more efficient and effective autonomous robot navigation.