Plant-inspired decentralized controller for robust orientation control of soft robotic manipulators

Area of Science:

• Robotics
• Control Systems
• Biomimetics

Background:

• Controlling the orientation of soft manipulators is challenging due to complex deformations and external disturbances.
• Existing control methods struggle with robustness in dynamic and interactive environments.

Purpose of the Study:

• To propose and validate a decentralized control strategy for robust orientation control of multi-segment soft manipulators.
• To draw inspiration from plant gravitropism for a novel control approach.

Main Methods:

• Developed a decentralized controller based on the principles of plant gravitropism.
• Validated the controller through simulations for convergence and robustness.
• Benchmarked the proposed controller against the traditional inverse Jacobian-based controller.

Main Results:

• The decentralized controller demonstrated comparable convergence to the inverse Jacobian-based controller.
• Achieved superior control precision and significantly better disturbance rejection.
• Exhibited robust control performance even with partial damage and human interaction.

Conclusions:

• The gravitropism-inspired decentralized controller offers a robust and effective approach for orientation control in soft manipulators.
• This strategy significantly enhances disturbance rejection capabilities in complex scenarios.
• Provides a foundation for advanced decentralized control in soft robotics applications.