Motion Planning and Iterative Learning Control of a Modular Soft Robotic Snake

Area of Science:

• Robotics
• Soft Robotics
• Biomimetic Engineering

Background:

• Snake robots offer versatility for inspection, search-and-rescue, and disaster response in complex environments.
• Existing snake robots often lack the adaptability and flexibility required for highly constrained or unstructured settings.
• Biological snakes provide a model for adaptable locomotion and environmental interaction.

Purpose of the Study:

• To introduce a novel soft pneumatic robotic snake with enhanced mobility and environmental adaptability.
• To develop and integrate advanced control and motion planning algorithms for autonomous operation.
• To demonstrate the robotic snake's capabilities in locomotion and obstacle avoidance.

Main Methods:

• Design and fabrication of a modular, pressure-operated soft pneumatic robotic snake.
• Integration of customized embedded flexible curvature sensing for proprioceptive feedback.
• Implementation of iterative learning control (ILC) for gait correction and adaptive bounding box motion planning for trajectory tracking.
• Experimental validation of locomotion and obstacle avoidance maneuvers.

Main Results:

• The soft robotic snake demonstrated effective locomotion and gait correction using ILC with curvature sensor feedback.
• The adaptive bounding box motion planning algorithm enabled efficient trajectory tracking and obstacle avoidance.
• The robotic platform showed high adaptability to the environment due to its soft body design.

Conclusions:

• The developed soft pneumatic robotic snake platform, combined with advanced control and planning, paves the way for autonomous soft robotic snake systems.
• This research advances the field of soft robotics, offering potential for improved performance in challenging real-world applications.
• The integration of sensing, control, and planning in a soft robotic system represents a significant step towards functional autonomy.