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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

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Automatic grasp planning for visual-servo controlled robotic manipulators.

F Janabi-Sharifi¹, W J Wilson

¹Dept. of Mech. Eng., Ryerson Polytech. Univ., Toronto, Ont.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 8, 2008

Summary

This summary is machine-generated.

This study presents an automatic grasp planner for robots using a single camera for vision-based grasping in dynamic environments. The approach effectively plans grasps for moving objects and robots, verified through simulations and experiments.

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Vision-based grasping is crucial for robotic manipulation in dynamic environments.
Existing methods often struggle with real-time adaptation to moving objects or robots.
End-effector mounted cameras offer unique advantages for close-range visual servoing.

Purpose of the Study:

To develop an automatic grasp planner (AGP) for visual servo controlled robots.
To address sensory, mechanical, and geometrical constraints in grasp planning.
To integrate visual feature selection for robust grasp candidate evaluation.

Main Methods:

A single camera mounted on the robot's end effector is utilized for visual feedback.
The approach incorporates quality measures to assess and rank potential grasps.
Implementation details and grasp planning strategies for the AGP are described.

Main Results:

The proposed automatic grasp planner demonstrates effectiveness in dynamic scenarios.
Simulation results validate the correctness of the grasp planning approach.
Experimental results confirm the system's ability to handle moving objects and robots.

Conclusions:

The developed automatic grasp planner is suitable for vision-based grasping in dynamic environments.
Integration of visual features and quality measures enhances grasp planning robustness.
The approach provides a viable solution for real-time robotic manipulation tasks.