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ILIAD Report Summary

Project ID: 732737
Funded under: H2020-EU.2.1.1.

Periodic Reporting for period 1 - ILIAD (Intra-Logistics with Integrated Automatic Deployment: safe and scalable fleets in shared spaces)

Reporting period: 2017-01-01 to 2017-12-31

Summary of the context and overall objectives of the project

ILIAD is driven specifically by the application needs for fleets of robots that operate in intralogistics 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. End users of all scales, from small enterprises to international corporations, are in need of robotic solutions that can integrate with current warehouse facilities, 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, for the foreseeable future, efficiently interact with human workers. 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 the remaining limitations in the state of the art which impede the efficient use of robot fleets in warehouse intralogistics. ILIAD addresses these limitations by a systematic study of human safety in shared environments and the development of a generic, safe and efficient solution for a fleet of heterogeneous robots handling intra-logistics tasks in human–robot shared environments, supporting life-long operation (meaning that the system can run independently over time, even when the environment changes), efficient methods for both centralised and distributed fleet coordination (of heterogeneous autonomous fleets mixed 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 on the European logistics industry, ILIAD has adopted a particularly challenging and demanding use case for intra-logistics in the distribution of fresh food products, involving autonomous AGVs operating in environments shared with human workers. For ILIAD, the fresh 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 fresh food distribution warehouse serves as a model for automating warehouse operations in many different and diverse 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. The particular focus of ILIAD is to enable an autonomous or mixed fleet of heterogeneous trucks to perform these tasks with low deployment costs, in a way that adapts to changing requirements and learns from observations so as to continuously optimise its own performance.

However, the expected impact of ILIAD also goes well beyond the intralogistics context. ILIAD develops key technologies that are relevant to all kinds of multiple-actor systems where robots and humans operate in the same environment – whether or not they are in a warehouse. We expect a sustainable scientific exploitation by extending the state of the art in the fields of robot perception (including reliability-aware mapping and learning of semantic maps), p

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the first year of ILIAD, the scientific work has focused on the following topics: reliable and precise self-localisation, foundational map representations for learning and maintaining long-term information about site-specific activity patterns, human–robot interaction, the development of a injury safety database, motion planning and centralised fleet coordination, and the design of a generally applicable gripping and handling solution.

Notable outcomes w.r.t. localisation include two novel methods that enable better localisation in otherwise difficult environments. We have also produced a prototype for automatic calibration of the ILIAD fleet’s range sensors; and demonstrated an efficient method for “semantic mapping”, by means of automatically grouping the 3D images that robots see into distinct places. We have also published work on novel map representations designed for long-term operation, which encode patterns of motion or other changes.

As for human–robot interaction, notable results include a people detection and tracking framework for both 2D and 3D range sensors, and the integration of novel safety camera hardware for reliable people detection in warehouse environments; as well as preliminary work on communicating robot intents by visual projections on the floor.

We have worked towards human safety by performing a hazard analysis for the ILIAD use case as well as a literature review regarding human injury biomechanics. Technically, we have a unified representation for biomechanics impact data and robot dynamic properties, which is to be used for safety-aware motion planning.

As for planning, we have published novel motion-planning algorithms: in particular, an algorithm that improves the efficiency of planning in tight spaces, and approaches that plan motions considering learned human behaviours.
We have also defined the requirements for the object manipulation system and implemented a preliminary embodiment, which has been used to perform physical and simulated experiments, picking and handling relevant goods from the ILIAD end users.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

ILIAD develops key technologies that are relevant to all kinds of systems where robots and humans operate in the same environment. The technologies to be researched within the scope of ILIAD will have impact in warehouse intralogistics as well as other applications of industrial robotics and automation. Crucial hindrances faced today by the industry in five domains are: dependability, vehicle types, efficiency and safety, the dynamic nature of the environment, and planning. These domains are completely covered by the objectives of the ILIAD project. Beyond the intralogistics context of the project, we expect a sustainable scientific exploitation by extending 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 in mixed human–robot environments. The project goals are very ambitious from a scientific point of view.

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