ILIAD is driven by the application needs for fleets of robots that operate in warehouse logistics applications with a high demand on flexibility, in environments shared with humans. In particular, the project aims to enable automatic deployment of a fleet of autonomous forklift trucks (AGVs), which will continuously optimise its performance over time by learning from collected data. Companies of all scales, from small enterprises to international corporations, are in need of robotic solutions that can integrate with current warehouses, thus facilitating a transition to automation. Small-scale end users with existing warehouses who do not have the means to build new, fully automated, warehouses will require smart automation that can effortlessly integrate with current facilities and efficiently and safely interact with staff. To this end, a system that is easily scalable is required: the overhead deployment cost of the first truck should be minimal, and additional trucks should seamlessly integrate with the existing fleet. More large-scale companies may invest in automated goods-to-person solutions that automatically deliver boxes to human pickers for preparation of customer orders. However, these systems lack the flexibility of traditional warehouses and cannot handle, e. g., oversized objects and dangerous goods. Therefore, solutions like those targeted by the ILIAD project will be required to efficiently handle the remaining fraction of goods, and thus continue the transition to automation for end users of all scales.
The overarching goal of ILIAD is to address limitations in the state of the art which impede the efficient use of robot fleets in warehouse logistics. We address these limitations by the following means: a systematic study of human safety in shared environments, the development of a generic, safe and efficient solution for a mixed fleet of robots handling logistics tasks in human–robot shared environments, supporting life-long operation (meaning that the system can run independently, even when the environment changes), efficient methods for fleet coordination (of mixed fleets of autonomous and human-driven vehicles), and automated picking and handling of a wide range of goods without replacing the gripper.
In order to drive the proposed research and innovations, and to maximise the impact of these actions within the industry, ILIAD has adopted a particularly demanding use case for logistics in the distribution of food products, involving AGVs operating together with human workers. For ILIAD, the food industry provides an especially relevant use case because of its particularly challenging requirements: sensitive products with short shelf-life, etc. The use-case scenario of a food distribution warehouse serves as a model for automating warehouse operations in many industries where rapid response to changing market needs is required. ILIAD aims to develop automated solutions to the complete range of tasks required for the intra-logistics chain in this type of scenario. However, the expected impact of ILIAD also goes well beyond the warehouse context. ILIAD develops key technologies that are relevant to all kinds of multiple-actor systems where robots and humans operate in the same environment. We expect to extend the state of the art in the fields of robot perception (including reliability-aware mapping and learning of semantic maps), planning (task allocation, coordination, motion planning), navigation, manipulation, and human–robot interaction. All of these innovations are essential for enabling independent, coordinated, safe and reliable operation of robots in shared human–robot environments.
