Design and control of a novel pneumatic soft robot based on the improved particle swarm optimization algorithm.

Area of Science:

• Robotics
• Bio-inspired Engineering
• Materials Science

Background:

• Soft robots offer advantages over traditional rigid robots, including enhanced flexibility, safety, and dexterity.
• Designing and controlling soft robotic systems presents significant engineering challenges.

Purpose of the Study:

• To propose a novel design approach for a bio-inspired quadruped soft robot.
• To develop a robust control strategy for enhanced locomotion and stability.

Main Methods:

• A hexagonal mesh structure and pneumatic actuators were designed for quadruped soft robot locomotion.
• A data-driven modeling approach was used for the soft actuator.
• An Improved Particle Swarm Optimization algorithm fine-tuned PID control parameters.

Main Results:

• The soft actuator model was validated through experimental implementation.
• The optimized control algorithm demonstrated effectiveness in improving performance.
• Tracking angle errors were reduced by over 50%, enhancing control accuracy and stability.

Conclusions:

• The proposed bio-inspired design and optimized control strategy are effective for quadruped soft robots.
• The study highlights the potential of soft robotics in achieving greater agility and precision.
• This research contributes to advancements in human-robot interaction and autonomous systems.