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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...

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Related Experiment Video

Updated: May 23, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

Enhanced Dynamic Window Approach for socially compliant robot navigation.

S Ashwath1, R Mayank1, S Pavithra1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.

Frontiers in Robotics and AI
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

Semantic-DWA enhances robot navigation by adding semantic understanding to the Dynamic Window Approach (DWA). This socially aware planner ensures safe human-robot coexistence by respecting proxemic boundaries, avoiding collisions.

Keywords:
dynamic window approachproxemicssemantic navigationsocial roboticssocially aware navigation

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Related Experiment Videos

Last Updated: May 23, 2026

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Published on: May 2, 2019

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Traditional local planners like Dynamic Window Approach (DWA) lack social awareness, leading to collisions.
  • Deep learning methods are computationally expensive for real-time navigation.

Purpose of the Study:

  • Introduce Semantic-DWA, a lightweight and interpretable framework.
  • Integrate semantic knowledge into traditional DWA for safer robot navigation.

Main Methods:

  • Utilize a perception function to classify obstacles (person, pet, object).
  • Implement social disqualification rules based on class-specific proxemic boundaries.
  • Evaluate Semantic-DWA in a Python-based 2D simulator.

Main Results:

  • Standard DWA resulted in collisions and proxemic violations.
  • Semantic-DWA achieved zero collisions in all simulations.
  • Maintained safe clearances: 1.00 m for persons, 2.08 m for pets.

Conclusions:

  • Semantic knowledge can be effectively added to local planners with minimal extensions.
  • Semantic-DWA offers a verifiable and predictable solution for safer human-robot coexistence.
  • Demonstrates the feasibility of incorporating social intelligence into robot navigation systems.