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Depth-Dependent Control in Vision-Sensor Space for Reconfigurable Parallel Manipulators.

Arturo Franco-López1, Mauro Maya1, Alejandro González2

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Summary
This summary is machine-generated.

A new control approach for reconfigurable parallel robots enables precise target tracking without prior trajectory knowledge. This method enhances robot capabilities by overcoming limitations like workspace constraints and singularities.

Keywords:
camera-space manipulationparallel robotvision-based control

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Area of Science:

  • Robotics
  • Control Systems
  • Computer Vision

Background:

  • Reconfigurable parallel robots offer advantages over traditional designs but present complex control challenges.
  • Existing control methodologies are not readily applicable to reconfigurable parallel manipulators.
  • Tracking unknown target trajectories is a critical but difficult task in many robotic applications.

Purpose of the Study:

  • To design and implement a novel control approach for reconfigurable parallel robots.
  • To enable accurate target tracking in vision-sensor, 3D, and joint spaces for a reconfigurable delta-type robot.
  • To address the lack of general control methodologies for reconfigurable parallel robots.

Main Methods:

  • Development of a control approach for reconfigurable parallel robots.
  • Implementation of controls in vision-sensor, 3D, and joint spaces.
  • Extension of the Velocity Linear Camera Model-Camera Space Manipulation (VLCM-CSM) methodology for vision-sensor space control.
  • Experimental validation on a reconfigurable delta-type parallel robot.

Main Results:

  • Achieved an average positioning error of 0.6 mm for static targets.
  • Obtained tracking errors of 2.5 mm, 3.9 mm, and 11.5 mm for linear trajectories at varying speeds.
  • Maintained a control cycle time of 16 ms.
  • Demonstrated superior performance compared to non-reconfigurable robot control.

Conclusions:

  • The proposed control approach is effective for reconfigurable parallel robots.
  • The method successfully enables accurate target tracking of unknown trajectories.
  • This work advances control strategies for complex, reconfigurable robotic systems.