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

Inspired by the flexible structures found in soft organisms in nature, researchers have developed a variety of novel soft robots using flexible materials. Compared to traditional rigid robots, soft robots offer advantages such as lighter weight, greater flexibility, higher degrees of freedom, and improved safety in human-robot interaction. However, designing and controlling soft robots remains a significant challenge. This paper proposed a novel design approach for a bio-inspired quadruped soft robot. Firstly, a hexagonal mesh structure for a quadruped soft robot was proposed, and the pneumatic actuator suitable for the soft structure was designed to enable the robot's leg movements, such as extension and bending. The body and overall structure of the robot were also systematically designed. Furthermore, a data-driven modeling method for the soft actuator was introduced, alongside an Improved Particle Swarm Optimization algorithm for fine-tuning PID control parameters. Finally, the prototype of the quadruped soft robot was constructed, and the control system was implemented. The proposed soft actuator model was validated, and the effectiveness of the proposed optimized algorithm was evaluated. Experimental results demonstrated that the application of the soft control model and the control parameter optimization algorithm reduced tracking angle errors by more than 50%, resulting in improved control accuracy and greater stability.