Artificial potential method for control of constrained robot motion

Area of Science:

• Robotics
• Control Theory
• Artificial Intelligence

Background:

• Robot control often requires detailed models, limiting adaptability.
• Navigating environments with obstacles and constraints presents significant challenges.
• Existing methods may lead to undesirable intermediate equilibriums, hindering task completion.

Purpose of the Study:

• To present a novel artificial potential method for constrained robot control.
• To develop a control strategy that does not require a detailed robot model.
• To enable robots to navigate complex environments and follow desired trajectories while respecting constraints.

Main Methods:

• Construction of an artificial potential field.
• Integration of obstacle avoidance and trajectory following.
• Ensuring the absence of undesirable equilibrium points.

Main Results:

• The proposed method successfully drives robots in environments with arbitrary obstacles.
• Robots can follow desired trajectories while adhering to constructive constraints.
• The method avoids intermediate equilibriums, preventing robots from getting locked.

Conclusions:

• The artificial potential method offers a model-free approach to constrained robot control.
• This technique enhances robot autonomy and adaptability in complex environments.
• The method provides a robust solution for trajectory tracking and obstacle avoidance.