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The superposition strategy for arm trajectory modification in robotic manipulators.

T Gat-Falik¹, T Flash

¹Dept. of Appl. Math. & Comput. Sci., Weizmann Inst. of Sci., Rehovot.

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

|February 7, 2008

Summary

This study introduces a superposition strategy for robot manipulator end-effector trajectory modification in response to unexpected target changes. This method ensures smooth motion by adding a new trajectory to the original, minimizing travel time under kinematic constraints.

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Area of Science:

Robotics
Control Systems
Human Motor Control

Background:

Dynamic tasks in robotics require robust end-effector trajectory modification for unexpected target location changes.
Human arm movement studies reveal effective strategies for adapting trajectories during dynamic tasks.
Existing methods may not adequately address real-time trajectory adjustments under kinematic constraints.

Purpose of the Study:

To present and discuss a novel superposition strategy for robot manipulator trajectory modification.
To adapt human arm trajectory modification principles for robotic systems.
To develop a method for optimizing temporal parameters to minimize travel time under kinematic constraints.

Main Methods:

The superposition strategy involves continuing the initial planned motion while vectorially adding a new trajectory to the original.
A method for selecting temporal parameters is proposed to minimize total travel time, considering joint and hand space constraints.
A variant of the strategy is presented for end-point orientation modification, not just position.

Main Results:

The superposition strategy allows for continuous motion adaptation to unexpected target shifts.
The proposed temporal parameter selection method optimizes movement efficiency.
The strategy is adaptable for both position and orientation control of the robot's end-effector.

Conclusions:

The superposition strategy offers an effective approach for real-time robot end-effector trajectory modification.
This method, inspired by human motor control, enhances adaptability in dynamic robotic tasks.
The approach provides a framework for minimizing travel time while respecting kinematic limitations.