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

Orthogonal Trajectories01:26

Orthogonal Trajectories

61
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
61
Reinforcement01:23

Reinforcement

919
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
919
Corrosion of Reinforcement01:27

Corrosion of Reinforcement

579
The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
However, over time and under certain conditions like carbonation, chloride ingress, and cracking this protective state can be compromised. Steel has areas with...
579
Reinforcement Schedules01:24

Reinforcement Schedules

504
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
504
Reinforcements in Concrete01:25

Reinforcements in Concrete

469
Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
469
Sampling Plans01:23

Sampling Plans

985
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
985

You might also read

Related Articles

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

Sort by
Same author

Atomic-to-Nanoscale Engineering of Piezocatalytic Heterojunctions for Ultrasound-Triggered Osteomyelitis Therapy.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Seasonal and spatial variations of microplastics in surface waters of Sansha Bay, a typical mariculture bay in the East China Sea.

Marine pollution bulletin·2026
Same author

Risk Factors for Postoperative Complications in Different Fusion Surgical Approaches for Lumbar Degenerative Diseases.

Journal of clinical medicine·2026
Same author

Nanoarmored Probiotic for Inflammatory Bowel Disease Bacteriotherapy via a Dual-Targeting Host-Microbiota Reprogramming.

ACS nano·2026
Same author

Physicochemical Parameters Govern Winter Pelagic Microbial Food Web Dynamics in China's Marginal Seas.

Microbial ecology·2026
Same author

A "Seesaw Effect" resolving the paradox in microzooplanktonic ciliate-environment interactions in the tropical Qinzhou Bay estuary-offshore continuum.

Marine environmental research·2026

Related Experiment Video

Updated: Feb 2, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

2.2K

Reinforcement Learning-Based Multi-AUV Adaptive Trajectory Planning for Under-Ice Field Estimation.

Chaofeng Wang1, Li Wei2, Zhaohui Wang3

  • 1Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USA. cwang8@mtu.edu.

Sensors (Basel, Switzerland)
|November 15, 2018
PubMed
Summary

This study introduces an online learning approach for multiple autonomous underwater vehicles (AUVs) to map underwater environments. The method optimizes AUV paths for efficient data collection and field estimation, achieving performance comparable to methods with prior knowledge.

Keywords:
AUVsadaptive trajectory planningfield estimationreinforcement learningunder-ice explorationunderwater communication networks

More Related Videos

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity
08:46

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity

Published on: January 15, 2014

9.6K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

958

Related Experiment Videos

Last Updated: Feb 2, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

2.2K
Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity
08:46

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity

Published on: January 15, 2014

9.6K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

958

Area of Science:

  • Robotics
  • Oceanography
  • Artificial Intelligence

Background:

  • Under-ice environments present unique challenges for underwater exploration and data collection.
  • Accurate estimation of water parameters is crucial for understanding these environments.
  • Autonomous underwater vehicles (AUVs) offer a solution for data acquisition in remote and hazardous locations.

Purpose of the Study:

  • To develop an online learning-based trajectory planning strategy for multiple AUVs.
  • To estimate a water parameter field of interest in an under-ice environment.
  • To optimize AUV sampling trajectories for efficient field characterization.

Main Methods:

  • A centralized system with ice-based access points for AUV communication.
  • Modeling the water parameter field using Gaussian processes with unknown hyper-parameters.
  • Formulating trajectory planning as a Markov decision process (MDP).
  • Employing a reinforcement learning-based algorithm for adaptive trajectory optimization.

Main Results:

  • The proposed algorithm determines AUV trajectories epoch-by-epoch.
  • Trajectories are optimized to reduce field uncertainty and minimize mobility costs.
  • Simulation results demonstrate performance comparable to a benchmark assuming perfect field knowledge.
  • The system effectively handles kinematics, communication, and sensing constraints.

Conclusions:

  • Online learning-based trajectory planning is effective for multi-AUV under-ice exploration.
  • The reinforcement learning approach enables adaptive path optimization in complex environments.
  • This method offers a viable strategy for estimating underwater parameter fields with unknown characteristics.