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

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

603
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...
603
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

738
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
738
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

686
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
686
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

432
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
432
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

608
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
608
Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

827
Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
827

You might also read

Related Articles

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

Sort by
Same author

Sight Guide demonstrates robotics-inspired vision assistance at the Cybathlon.

Science robotics·2025
Same author

Low-latency automotive vision with event cameras.

Nature·2024
Same author

Wearable robots for the real world need vision.

Science robotics·2024
Same author

Champion-level drone racing using deep reinforcement learning.

Nature·2023
Same author

Agilicious: Open-source and open-hardware agile quadrotor for vision-based flight.

Science robotics·2022
Same author

AlphaPilot: autonomous drone racing.

Autonomous robots·2022

Related Experiment Video

Updated: Dec 6, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.4K

Dynamic obstacle avoidance for quadrotors with event cameras.

Davide Falanga1, Kevin Kleber2, Davide Scaramuzza2

  • 1Department of Informatics, University of Zurich, Zurich, Switzerland. falanga@ifi.uzh.ch.

Science Robotics
|October 6, 2020
PubMed
Summary
This summary is machine-generated.

Autonomous drones now use event cameras for microsecond reaction times, enabling safe navigation around fast obstacles. This low-latency system achieves 3.5-millisecond reaction times for reliable obstacle avoidance.

More Related Videos

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

2.9K
High-Resolution Video Tracking of Locomotion in Adult Drosophila Melanogaster
09:08

High-Resolution Video Tracking of Locomotion in Adult Drosophila Melanogaster

Published on: February 20, 2009

13.8K

Related Experiment Videos

Last Updated: Dec 6, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.4K
Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

2.9K
High-Resolution Video Tracking of Locomotion in Adult Drosophila Melanogaster
09:08

High-Resolution Video Tracking of Locomotion in Adult Drosophila Melanogaster

Published on: February 20, 2009

13.8K

Area of Science:

  • Robotics
  • Computer Vision
  • Bio-inspired Engineering

Background:

  • Current autonomous drones have reaction times insufficient for dynamic environments.
  • Fast-moving object avoidance requires low-latency sensors and algorithms.

Purpose of the Study:

  • To develop a low-latency system for autonomous drone navigation and obstacle avoidance.
  • To utilize event cameras for real-time environmental perception.

Main Methods:

  • Employed event cameras, bio-inspired sensors with microsecond reaction times.
  • Developed algorithms to process asynchronous event streams, distinguishing static and dynamic objects.
  • Implemented a fast strategy for generating motor commands for obstacle avoidance.

Main Results:

  • Achieved an overall system latency of 3.5 milliseconds.
  • Successfully demonstrated reliable detection and avoidance of fast-moving obstacles (up to 10 m/s).
  • Validated the approach on an autonomous quadrotor using only onboard processing.

Conclusions:

  • Event cameras and specialized algorithms enable significantly reduced latency for autonomous drones.
  • The developed system provides a viable solution for safe navigation in complex, dynamic environments.
  • Onboard sensing and computation are sufficient for real-time obstacle avoidance with this approach.