Research on motion planning for an indoor spray arm based on an improved potential field method

Area of Science:

• Robotics
• Agricultural Engineering
• Control Systems

Background:

• Spray robot performance relies heavily on the spraying arm's aiming and tracking capabilities.
• Improving endpoint control robustness and positioning accuracy is crucial for efficient agricultural operations.

Purpose of the Study:

• To propose an improved potential field algorithm for enhanced spray robot arm motion planning and control.
• To increase the robustness and positioning accuracy of spray arm endpoint control.

Main Methods:

• An improved potential field algorithm integrating velocity potential field, visual field, and joint position constraints.
• Planning feasible trajectories in both Cartesian and image spaces.
• Utilizing a speed controller for trajectory-based spray arm movement.

Main Results:

• The algorithm successfully planned superior motion trajectories compared to previous image moment-based servo controllers.
• Experimental results demonstrated significant improvements in the robustness of targeting and tracking control.
• The velocity potential field ensured stable target tracking by matching relative velocities.

Conclusions:

• The proposed algorithm effectively enhances the targeting and tracking control robustness of spray robots.
• Integration of velocity potential field and constraint factors improves overall spray arm performance.
• This approach offers a more robust solution for precision agriculture applications.