Dynamic modelling and predictive position/force control of a plant-inspired growing robot

Area of Science:

• Robotics
• Control Systems
• Biologically Inspired Engineering

Background:

• Complex environments pose challenges for traditional robots.
• Biologically inspired robots offer novel solutions for navigation and manipulation.
• Growing robots, like the vine-robot, present unique dynamic modeling and control problems.

Purpose of the Study:

• To develop and control a dynamic model for a plant-inspired growing robot (vine-robot).
• To enable the vine-robot to navigate complex environments using its unique growth mechanism.
• To regulate the robot's task space position, orientation, and interaction forces.

Main Methods:

• Utilized the Euler-Lagrangian method for dynamic modeling.
• Derived equations of motion for the vine-robot.
• Employed model predictive control (MPC) for regulation tasks.

Main Results:

• Achieved sub-millimeter precision in position control for static and time-varying trajectories.
• Demonstrated sub-micronewton precision in force control.
• Validated the proposed dynamic model and control strategy through simulations.

Conclusions:

• The developed dynamic model and MPC strategy are effective for controlling the vine-robot.
• The vine-robot shows high precision in position and force control, suitable for complex environments.
• This research contributes to the advancement of growing robots and autonomous systems.