Operation Planning and Closed-Loop Control of a Soft Robotic Table for Simultaneous Multiple-Object Manipulation

Abstract

This article presents the design, development, and validation of a closed-loop control system for a soft robotic table. The soft table can provide independent manipulation to multiple objects simultaneously, which is an advantage over traditional devices such as robotic arms and belt conveyor systems. The table also provides intrinsic soft handling for delicate objects. In order to execute automation tasks, a vision feedback system and an operation controller are developed and incorporated into the existing table prototype to form a closed-loop control. The vision feedback system implements image processing techniques to identify the object location from captured images. The operation controller is integrated from three sets of algorithms, including movement planning, actuator selection, and actuation planning. Two sets of experiments were conducted to validate the closed-loop system and the capability of the soft table. The first set validated the manipulation task of transporting an object to a desired location with autonomous planning and execution. The second set validated the table’s ability to manipulate multiple objects simultaneously and independently. Videos of both experiments have been supplied. Note to Practitioners —A soft robotic table is developed for the manipulation of delicate objects on a surface. The table is intended for automated sorting tasks in an industrial environment. Multiple objects placed on the table surface can be simultaneously and independently transported and orientated on the $XY$ horizontal plane. The deformable surface of the table provides soft handling for delicate objects. These are the advantages over existing manipulators such as robotic arms and belt conveyor systems. Based on previously conducted experiments, it was found that the open-loop control is ineffective to execute automation tasks due to the variability in object movements with soft interactions. To solve this problem, a closed-loop control system for autonomous object manipulation is developed and its detailed design and implementation are presented in this article. A vision feedback system and a controller are developed to form the proposed closed-loop control. Two sets of experiments involving common manipulation tasks were conducted. The control system is proven to be effective at handling the uncertainties in object movements and autonomous object manipulation by the soft robotic table is validated. The performance of this control system can be improved with better predictions of both the deformations on the table surface and the resultant object movements. Embedding deformable sensors into the table surface is a potential approach to achieve such improvement and this can be investigated in future works.