Pick and place are basic operations in most robotic applications, whether in industrial setups (e.g. machine tending,assembling or bin picking) or in a service robotic domain (e.g. agriculture or at home). In some structured scenarios and
with certain types of parts, picking and placing is a mature process. However, that is not the case when it comes to manipulating parts with high variability or in less structured environments.
Handling systems are present in any logistics system to interface between the storage and the transportation systems. For non-structured scenarios, picking, packing and unpacking systems do exist at laboratory level. However, they have not reached the market yet due to factors like the lack of efficiency, robustness and flexibility of currently available manipulation and perception technologies.
The market demands systems that allow for a reduction of costs in the supply chain, increasing the competitiveness for manufacturers and bringing a cost reduction for consumers. Handling systems represent the highest impact in the shorttomidterm in warehouse-based systems (mainly at order picking and distribution centres) and in intra-logistics operations in factories and retail.
The technology gap is the lack of flexible solutions that can handle objects of variable size, shape and weight as well as different surface properties and stiffness.
PICKPLACE proposes combining human and robot capabilities to achieve a safe, flexible, dependable and efficient hybrid pick-and-package (PAK) solution. It includes dynamic package configuration planning, flexible grasping strategies using an innovative multifunctional gripper, robust environment perception and mechanisms and strategies for safe human-robot collaboration.
The Technological and Industrial objectives (TO and IO) identified are:
TO 1: To develop a new generation of multifunctional grippers to handle products of different morphology, weight and rigidity and that are able to reach difficult to access target positions
SO 1: To develop reactive grasp-planning algorithm based on cognitive capabilities and allows the robot to effectively grasp different objects
SO 2: Robust and efficient bin-picking solution based on object pose identification and fast and safe robot path planning
SO 3: Human and robot affordance aware dynamic package planning for mono and multireference configurations.
SO 4: Dynamic robot planning based on cognitive capabilities exploiting perceived monitoring and human activity.
SO 5: Reliable environment perception system and strategies for safe collaborative scenarios based on Speed and Separation Monitoring combined with Power and Force Limiting.
IO 1: To increase the pick-and-package global performance in terms of flexibility, dependability and error reduction.
IO 2: Improvement of the working conditions of operators by a proper layout design and task allocation between worker and robot.
