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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Updated: Jun 25, 2025

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Programming tunable active dynamics in a self-propelled robot.

Somnath Paramanick1, Arnab Pal2,3, Harsh Soni4

  • 1Department of Physics, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India.

The European Physical Journal. E, Soft Matter
|May 23, 2024
PubMed
Summary
This summary is machine-generated.

We developed a robotic device capable of tunable active dynamics, mimicking particle models like active Brownian motion. This controllable robot navigates obstacles using light gradients, advancing active matter physics and bio-inspired robotics.

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Area of Science:

  • Robotics
  • Active Matter Physics
  • Biophysics

Background:

  • Active matter systems exhibit complex dynamics inspired by biological organisms.
  • Controlling the motion of artificial active matter is crucial for understanding fundamental physics and developing novel applications.
  • Robotic devices offer a platform to experimentally investigate active matter models.

Purpose of the Study:

  • To design and implement a self-propelled robotic device with tunable active dynamics.
  • To demonstrate the robot's ability to replicate various active particle models.
  • To explore light-controlled navigation and obstacle avoidance using stochastic reorientation.

Main Methods:

  • Utilizing a differential drive mechanism for independent wheel velocity control.
  • Calculating robot velocities by equating 2D equations of motion with active particle models.
  • Encoding control algorithms into the robot's microcontroller.
  • Analyzing robot trajectories using particle tracking and comparing with theoretical predictions.

Main Results:

  • The robot successfully depicted active Brownian, run and tumble, and Brownian dynamics across a range of parameters.
  • Experimental trajectories showed excellent agreement with theoretically predicted motion.
  • Robot dynamics were switched between different models using light intensity as an external control parameter.
  • The robot demonstrated efficient obstacle navigation via light-gradient-driven stochastic reorientation.

Conclusions:

  • A tunable active robotic system was successfully developed, capable of mimicking diverse active matter behaviors.
  • Light intensity serves as an effective external parameter for controlling robot dynamics and enabling navigation.
  • This work provides a platform for studying active matter physics and developing bio- and nature-inspired robotic systems.