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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Updated: Nov 19, 2025

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A Two Teraflop Swarm.

Simon Jones1,2,3, Matthew Studley2,3, Sabine Hauert1,3

  • 1University of Bristol, Bristol, United Kingdom.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

The Xpuck swarm, a powerful research platform, uses 16 augmented e-puck robots to achieve over two teraflops of processing power. This enables advanced swarm robotics experiments, including complex simulations and real-world applications like SLAM.

Keywords:
GPGPUbehavior treesdistributed evolutionary algorithmembodied reality modellingevolutionary roboticsrobot hardwaresimulationswarm robotics

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Distributed Computing

Background:

  • Existing swarm robotics platforms lack the computational power for complex, real-time tasks.
  • Modern mobile system-on-chip (SoC) devices offer significant untapped CPU and GPU resources.

Purpose of the Study:

  • Introduce the Xpuck swarm, a novel research platform with unprecedented aggregate processing power.
  • Enable new experiments in swarm intelligence, distributed computing, and advanced robotics applications.

Main Methods:

  • Augmenting 16 e-puck robots with custom hardware leveraging mobile SoC capabilities.
  • Developing a distributed island model evolutionary system hosted entirely on the Xpuck swarm.
  • Implementing a fast physics-based robot simulator on the swarm.

Main Results:

  • Achieved an aggregate raw processing power exceeding two teraflops.
  • Demonstrated the swarm's computational capability through a complex evolutionary simulation.
  • Xpucks exhibit an order of magnitude performance increase over prior swarm platforms.

Conclusions:

  • The Xpuck swarm provides a powerful, versatile platform for advanced swarm robotics research.
  • Facilitates experiments requiring high individual robot computation, such as online learning and SLAM.
  • Offers a unique capability for exploring natural swarming behaviors with insect-level computational power.