Periodic Reporting for period 1 - SoftGrip (Functionalised Soft robotic gripper for delicate produce harvesting powered by imitation learning-based control)
Reporting period: 2021-01-01 to 2022-06-30
The fresh food industry is under growing pressure to reduce production costs due to high levels of competition. Moreover, in the specific case of mushroom industry, it is highly labour-intensive, with much of the manual work requiring rapid, repetitive, and monotonous movement that lead to poor quality control and a high incidence of industrial accidents. Therefore, the food industry is looking increasingly towards automation and robotics to help lower production costs and improve job quality and safety. However, the capabilities of the human hand in terms of dexterity, sensitivity and precision are still unmet.
So, the main objective of the SoftGrip project is to develop a self-actuating soft gripper for the autonomous picking of delicate white button mushrooms.
We are studying and developing low-cost, soft robotic grippers having built-in actuation, sensing and embodied intelligence that enable safe-grasping, adaptability to object shape and size, and grasping versatility for reliable and efficient picking of mushrooms. We are engineering blending of novel materials that offer precise tuning of fundamental material properties, that interact safely with the food, with minimum impact on the environment and that provide robust and maintenance-free production over many cycles of operation. We are using a set of accelerated continuum mechanics modelling algorithms that will facilitate sophisticated model-based control schemes. Moreover, we are investigating advanced cognition capabilities in the soft gripper through a learning by demonstration framework.
So, the main objective of the SoftGrip project is to develop a self-actuating soft gripper for the autonomous picking of delicate white button mushrooms.
We are studying and developing low-cost, soft robotic grippers having built-in actuation, sensing and embodied intelligence that enable safe-grasping, adaptability to object shape and size, and grasping versatility for reliable and efficient picking of mushrooms. We are engineering blending of novel materials that offer precise tuning of fundamental material properties, that interact safely with the food, with minimum impact on the environment and that provide robust and maintenance-free production over many cycles of operation. We are using a set of accelerated continuum mechanics modelling algorithms that will facilitate sophisticated model-based control schemes. Moreover, we are investigating advanced cognition capabilities in the soft gripper through a learning by demonstration framework.
During the first period, the consortium work was mainly focused on knowledge exchange, definition of requirements and fundamental research on stand-alone subsystems and simple prototypes. Despite in-person hands-on meetings were limited by the pandemic situation, the consortium also moved first steps towards an early integration of the full platform on a realistic scenario.
Manipulating fragile and delicate objects is a difficult task for a robotic device, especially because it involves multiple interconnected features and abilities: delicate yet firm grasp, motion/deformation capabilities, effective and adaptable motion execution, advanced sensing, durability, contact safety, just to name the most important. In this context, mushroom harvesting stands as a very challenging scenario and as a paradigmatic example to explore new approaches and technological routes. There exist some ongoing studies on the field, but with limited success so far, especially regarding the achievable produce quality. The main expected innovation consists of an advanced robotic platform that exploits a multidisciplinary approach combining material science, soft mechatronics, advanced vision algorithms, and cutting-edge learning techniques.
The project is expected to have an impact on three main topics: (I) In the specific scenario of mushroom harvesting, we are expecting to enable a step change in efficiency, helping mushroom growers cut down on costs and increase their yields and at the same time, this will increase job quality and safety by reducing the strenuous part of mushroom harvesting; (II) More in general, we will contribute to answer fundamental questions on manipulation abilities and on skill transfer through meta-learning techniques and we are expecting to develop new technologies for delicate yet effective manipulation; (III) In the long-term, the achievements of the project could be extended to open-up new opportunities for adoption of robotic solutions in other sectors and used to lower the barriers of robotics deployment.
The project is expected to have an impact on three main topics: (I) In the specific scenario of mushroom harvesting, we are expecting to enable a step change in efficiency, helping mushroom growers cut down on costs and increase their yields and at the same time, this will increase job quality and safety by reducing the strenuous part of mushroom harvesting; (II) More in general, we will contribute to answer fundamental questions on manipulation abilities and on skill transfer through meta-learning techniques and we are expecting to develop new technologies for delicate yet effective manipulation; (III) In the long-term, the achievements of the project could be extended to open-up new opportunities for adoption of robotic solutions in other sectors and used to lower the barriers of robotics deployment.