Design optimisation and an experimental assessment of soft actuator for robotic grasping

Many robotic systems face substantial challenges when trying to grasp and manipulate objects. Thought of initially as humanoid automata a century ago, this viewpoint is still influential in modern robot design. Many robotic grippers are inspired by the deftness of the human hand. The perceptual, processing, and control issues that conventional grippers have are also experienced by soft-fingered grippers. Precise finger placement, dictated by the shape and attitude of the object, is necessary to accomplish force closure when using soft fingertips to grasp. Soft robotic end-effectors have several advantages, such as a good interface with humans, the capacity to adapt to different environments, a number of degrees of freedom, and the ability to non-destructively grasp items of various shapes. Adding to earlier research that looked at the soft robot in a theoretical way, this study creates an optimized model based on the deformation in terms of bending of the channel cavity under applied pneumatic pressure. A correlation between pneumatic pressure and the pneumatic soft actuator's bending angle has been demonstrated. This research looks at how different design factors affect the bending of a multi-chambered soft actuator that is pneumatically networked. The finite element approach involves fine-tuned (optimised) actuator construction. Using FEM to evaluate aspects affecting actuator mechanical output, the ideal design parameters were discovered using DoE, resulting in a bending angle of ~ 104 degrees at 30 kPa. This study used ANOVA at a 5% significant level to identify which variables most affected the pneumatic actuator's deformation (bending angle). The significant R-square value of 96.42% supports the study's conclusions that the parameters utilised explain an immense percentage of bending angle deviations. Experimental verification of the optimized finite element model findings was conducted. The verification of the actuators' bending angles and output forces reveals that the discrepancy between the two sets of data stayed below 9%. Also, the average gripping success rate attained in the grasping evaluation, which involved four distinct types of items, was almost 97%.