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Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Decision Making: Traditional Method01:14

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One-Degree-of-Freedom System01:24

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Related Experiment Video

Updated: Jun 8, 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

Choosing autonomy modes for multirobot search.

Michael Lewis1, Huadong Wang, Shih Yi Chien

  • 1University of Pittsburgh, Department of Information Science, Pittsburgh, PA 15260, USA. ml@sis.pitt.edu

Human Factors
|October 15, 2010
PubMed
Summary
This summary is machine-generated.

Robot navigation is the limiting factor in multirobot foraging tasks. Automating robot navigation can improve operator supervision of multiple robots in search and identification applications.

Related Experiment Videos

Last Updated: Jun 8, 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

Area of Science:

  • Robotics
  • Human-Robot Interaction
  • Artificial Intelligence

Background:

  • Multirobot systems are key to future robotic applications.
  • Foraging is a fundamental multirobot task involving independent exploration and target discovery.
  • Operator capacity for managing robots varies with autonomy and task complexity, typically ranging from 3 to 12 robots.

Purpose of the Study:

  • Investigate multirobot foraging to determine which task aspects are most suitable for automation.
  • Identify performance limitations in multirobot foraging as a function of team size and task subdivision.

Main Methods:

  • Compared full-task foraging performance with isolated exploration and perceptual search tasks.
  • Utilized a between-groups repeated measures design with 4-, 8-, and 12-robot teams.
  • Assessed operator performance and workload across different conditions.

Main Results:

  • Operators could not effectively manage 12 robots in the full-task foraging scenario.
  • Exploration performance was consistent across full-task and dedicated exploration conditions.
  • Navigation (exploration) was identified as the bottleneck limiting operator control in multirobot foraging.

Conclusions:

  • Robot navigation and exploration tasks are the primary limiting factors in multirobot foraging.
  • Automating robot navigation is recommended to enhance operator supervision capabilities.
  • Task-centered automation architectures are supported, focusing on automating control and coordination for applications like foraging.