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A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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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.
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Optimal Path Planning Algorithm with Built-In Velocity Profiling for Collaborative Robot.

Rafal Szczepanski1, Krystian Erwinski1, Mateusz Tejer1

  • 1Department of Automatics and Measurement Systems, Institute of Engineering and Technology, Faculty of Physics Astronomy and Informatics, Nicolaus Copernicus University, Wilenska 7, 87-100 Torun, Poland.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

This study introduces a novel path planning method for collaborative robots, optimizing production cycles. The approach significantly reduces cycle time for tasks involving delicate or liquid-filled items.

Keywords:
B-splinecollaborative robotnature-inspired optimization algorithmpath planning problempick-and-placevelocity profile

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Path planning is crucial for efficient robotic operations, especially in manufacturing.
  • Existing methods may not adequately address time-optimality and smoothness for complex tasks.
  • Collaborative robots require advanced planning for safe and efficient human-robot interaction.

Purpose of the Study:

  • To develop a time-optimal, smooth, and collision-free path planning method for collaborative robots.
  • To enhance the efficiency of production cycles involving delicate item manipulation.
  • To minimize execution time while respecting robot motion constraints.

Main Methods:

  • Utilized a nature-inspired optimization algorithm to generate B-spline paths.
  • Developed a velocity profiling algorithm for B-spline trajectories.
  • Applied the methodology to optimize a pick-and-place process for a collaborative robot.

Main Results:

  • The proposed path planning algorithm reduced production cycle time by 11.28% compared to point-to-point movement.
  • Achieved a 57.5% reduction in cycle time compared to the RRT* algorithm with identical motion constraints.
  • Demonstrated significant improvements in smoothness and efficiency through simulation and experimental validation.

Conclusions:

  • The proposed method offers a superior approach to path planning for collaborative robots, particularly for tasks requiring precision and speed.
  • The optimization significantly decreases production cycle times, enhancing overall manufacturing efficiency.
  • Experimental validation confirms the practical applicability and effectiveness of the developed algorithm.