Because of the increasing e-commerce volume, resulting in increasing demands on the speed of delivery, logistical processes become more and more automated. Order picking is one of the last tasks that is done by humans in warehouses, because humans are flexible with respect to the large variability and changeability in items. However, it is a labour intensive and monotonous task, resulting in fatigue and a shortage of order picking personnel. This motivates the development of automated pick-and-place systems.
One of the challenges for such systems is the heterogeneity of items. In warehouses there is a big diversity in items so the system has to be able to deal with all of them. Another challenge is dealing with items that are deformable. Current systems often make use of suction cups but integrating sensors that can be used to handle deformable items is hard. Fingered robotic grippers have more potential in grasping these kind of items, but grasping deformable items is one of the least addressed topics in robotics. Therefore, the objective of this thesis is to design a control strategy for a fingered robotic gripper to grasp and hold deformable items in a pick-and-place task.
Inspired by the underlying principles that humans use to execute a pick-and-place task, a multi-level controller is proposed for a three-fingered gripper with capacitive pressure pads. The multi-level controller consists of a low-level computed torque controller and a high-level numerical optimisation based extremum seeking controller. The computed torque controller uses an internal model of the kinematics and dynamics, which is derived with screw theory, to compute the torques required to comply with the fundamental grasping constraint and the setpoint on the gripping force. The controller is tuned in such a way that the grasp quality is maximised, given a constant reference gripping force. Because of the fact that the properties of the items are unknown, an intelligent control system has to be able to determine the gripping force setpoint autonomously. This is the task of the high-level controller, that uses tactile sensors to derive the slip. This slip is used to determine the setpoint on the gripping force that the low-level controller has to follow, while maximising the grasp quality and not damaging the products as a result of applying excessive gripping force.
The proposed control strategy is tested and tuned in a simulation environment. The pick-and-place task is executed for the products from a virtual product inventory. The controller is optimised with respect to the control goal on a wide variety of deformable items. Designing controllers according to the proposed principle will increase the diversity of items that can be handled in a pick-and-place environment, while increasing the quality of the grasp and minimising the risk of damaged products.