Robotics is revolutionizing logistics applications. However, robot manipulation abilities are still far from human level of dexterity. Today, robots are not yet able to perform dynamic manipulation tasks such as filling densely and quickly a box with items or swiftly grasping heavy boxes from a pallet. The current boundary of state-of-the-art robot control is reached when contact is established at non-zero speed and simultaneously at multiple locations and when objects are heavy with respect to the robot own mass (e.g. lifting a 10 Kg box with two arms establishing contact at a speed higher than 0.1 m/s). Contacts established in these conditions lead to impacts, physical interactions characterized by fast changes in robot and object velocities and, at impact times, large peaks of the interaction forces. Current limitations in performing dynamic manipulation tasks are caused by the inability to reliably predict the effect of robot-object impacts as well as use these predictions for learning, sensing, and control impact manipulation motions. As a response to these challenges, I.AM. will (1) validate robot-object impact models to predict the result of a collision, (2) learn dynamic motions with impacts to achieve user specified goals, (3) provide robust sensing of robot-object post-impact velocities, contact forces and contact state, and (4) allow for robust robot control of dynamic manipulation tasks. Finally, I.AM. will (5) demonstrate this impact aware manipulation technology in relevant logistics scenarios with socio-economic impact. Regulation surrounding construction and logistics is becoming even more restrictive. Regulations that guarantee continuous improvement of working conditions has always been the aim of the trade unions. Action has been taken in this direction. For example, working time reduction, total weight limit to be handled within a working shift, height limitations in the loading of container (impact on the capacity utilization rate). These regulatory restrictions provide an incentive to develop robotic technology to ensure that workers are safe and working within regulations. In the logistic sector, the job of workers that do take-put processes is physically demanding work, where operators need handle items up to 15 kg, in 4 hour shifts. I.AM. will create a better business case for automating more processes in the logistics domain. Automation of up to 5% more processes in typical retail and parcel distribution centers and even up to 45% more processes in typical E-commerce warehouses are estimated by Vanderlande to have a sound business case through I.AM technology. With this increased automation of take-put processes that involves lifting of items and cases up to 15kg, the amount of injuries in the work force will be significantly lowered. Furthermore, I.AM. will trigger the attention of several robotic manufacturers on the potentials of developing an impact aware manipulation technology for robots and thus create new market and job opportunities. Because I.AM. builds on different robotic domains, a multi-level impact is expected: (1) Improved impact modeling and simulation will allow to increase the performance of next generation torque-controlled robots, by allowing reliable simulations with impacts considered in an early engineering stage and guiding design decisions; (2) better understanding and modeling of dynamic interaction will help to increase the performance on consolidated applications currently addressed with simpler static/quasi static manipulation; (3) Improved planning and sensing will allow implementing new tasks of high dynamic performance opening the way to new and emerging applications; (4) Greater availability and easy adoption of simulation technology thanks to simple interfacing with controller libraries.
