=Paper=
{{Paper
|id=Vol-1834/paper8
|storemode=property
|title=A Timeline-based Planning System for Human-Robot Collaboration in Manufacturing Domains
|pdfUrl=https://ceur-ws.org/Vol-1834/paper8.pdf
|volume=Vol-1834
|authors=Amedeo Cesta,Giulio Bernardi,Andrea Orlandini,Alessandro Umbrico
|dblpUrl=https://dblp.org/rec/conf/aiia/CestaBOU16
}}
==A Timeline-based Planning System for Human-Robot Collaboration in Manufacturing Domains==
https://ceur-ws.org/Vol-1834/paper8.pdf
A Timeline-based Planning System for Human-Robot
Collaboration in Manufacturing Domains
Amedeo Cesta1 , Giulio Bernardi1 , Andrea Orlandini1 , and Alessandro Umbrico2
1
Consiglio Nazionale delle Ricerche
Istituto di Scienze e Tecnologie della Cognizione
2
Università degli Studi Roma TRE
Dipartimento di Ingegneria
Abstract. Industrial robots have demonstrated their capacity to meet the needs
of many applications, offering accuracy and efficiency. However, when robot-
worker collaboration is needed, safety represents a key aspect and needs to be
enforced in a comprehensive way. In this regard, seamless and safe human-robot
collaboration still constitutes an open challenge in manufacturing. FourByThree
is an ongoing research project funded by the European Commission and aimed to
design, build and test pioneering robotic solutions able to collaborate safely and
efficiently with human operators in industrial manufacturing companies. The pa-
per presents the ongoing work in the project related to a task planning framework
specifically designed and tailored to address the challenges related to human-
robot collaborative production processes.
1 Motivations and Context
Industrial robots have demonstrated their capacity to meet the needs of many appli-
cations, offering accuracy and efficiency. However, when robot-worker collaboration
is needed, safety represents a key aspect and needs to be enforced in a comprehen-
sive way. In this regard, seamless and safe human-robot collaboration still constitutes
an open challenge in manufacturing. FourByThree [1] is an ongoing research project3
aimed to design, build and test pioneering robotic solutions able to collaborate safely
and efficiently with human operators in industrial manufacturing companies. Its overall
aim is to respond to the above challenge by creating a new generation of robotic solu-
tions, based on innovative hardware and software, which present four main character-
istics: modularity, safety, usability and efficiency And considers three different actors:
humans, robots and the environment.
The resulting robotic solutions of the project will be tested in four pilot imple-
mentations, which correspond to real industrial needs and are representative of the two
possible robot-human relationships in a given workplace without physical fences: coex-
istence (human and robot conduct independent activities) and collaboration (they work
collaboratively to achieve a given goal). During the project, two different categories of
pilot studies are considered. Three pilots correspond to production industries related
to different realistic scenarios in which robotic co-workers are considered to perform
3
http://www.fourbythree.eu
assembly/disassembly tasks, conventional production tasks (e.g., deburring, welding,
etc.) and working processes involving large parts. The fourth pilot study will be used as
a living lab for experimenting with a big number of subjects, mainly during the devel-
opment process.
This extended abstract presents the ongoing work in the project related the definition
of safety strategies and control mechanisms generated by means of a task planning
framework [2] specifically designed and tailored to address the challenges related to
human-robot collaborative production processes.
2 Human-Robot Collaborative Scenarios
A human-robot collaboration workcell can be considered as a bounded connected space
with two agents located in it, a human and a robot system, and their associated equip-
ment [3]. A robot system in a workcell consists of a robotic arm with its tools, its base
and possibly additional support equipment. The workcell also includes the workpieces
and any other tool associated with the task and dedicated safeguards (physical barri-
ers and sensors such as, e.g., monitoring video cameras) in the workcell space. In such
workcell, different degrees of interaction between a human operator and the robot can
be considered [4]. In all these cases, it is assumed that the robot and the human may
need to occupy the same spatial location: Independent, the human and the robot oper-
ate on separate workpieces without collaboration, i.e., independently from each other.
Synchronous, the human and the robot operate on sequential components of the same
workpiece, i.e., one can start a task only after the other has completed a preceding task.
Simultaneous, the human and the robot operate on separate tasks on the same work-
pieces at the same time. Supportive, the human and the robot work cooperatively in
order to complete the processing of a single workpiece, i.e., they work simultaneously
on the same task. Different interaction modalities requires the robot endowed with dif-
ferent safety settings while executing tasks.
3 Dynamic Task Planning for Safe Human-Robot Collaboration
As part of the overall FourByThree (ROS-based) control architecture, a dynamic task
planner is to provide continuous task synthesis features, safety critical properties at
execution time, and user modeling ability for adapting tasks to the particular human at
work. The integration of plan synthesis and continuous plan execution has been demon-
strated both for timeline based planning (e.g., [5]) and PDDL based (e.g., [6]). In sce-
narios of human robot interaction important problems have been addressed: (a) ”human
aware” planning has been explored for example in [7], (b) the interaction of background
knowledge for robotic planning in rich domain (addressed for example in [8], (c) syn-
thesis of safety critical plans to guarantee against harmful states (relevant in co-presence
with humans) is addressed in [9] and [10]). Within the FourByThree project, a timeline-
based planning approach is pursued relying on the APSI-TRF software infrastructure
[11], made available by European Space Agency, and improved from the initial pro-
posal [12] and its test in several missions. Then, a FourByThree planning framework
has been designed to deploy a continuous task planning and adaptation system with
humans in the loop.
Production Task Decomposition
Knowledge
Engineer Knowledge
Engineer
Eng. Services
V&V Services
HR Coll.
Process
Worker Task
4x3
Preferences/ Planning
Model
Task Planner
Commands Task Plan
HMI
Execu1ve
System
4x3 Archi
(ROS)
Worker
Fig. 1. Dynamic Task Planning Framework in FourByThree.
The overall framework is depicted in Figure 1. A Production Engineer is in charge of
defining the Human-Robot collaborative (HRC) production process characterizing each
task according to specific HRC settings (i.e., interaction modalities). Then, a Knowl-
edge Engineer is to encode such information in a task planning model following a
hierarchical decomposition and leveraging the features provided by an environment
for Knowledge Engineering of Planning with Timelines, called K EE N [13], that in-
tegrates “classical” knowledge engineering features with Verification and Validation
(V&V) formal techniques to perform domain model validation, planner validation,
plan verification, etc. The integration of Planning and Scheduling (P&S) technology
with V&V techniques is key to synthesize a safety critical controller for the robot. The
Task Planning Model can be, then, adapted also according to the preferences of the
Human Worker that is supposed to interact with the robot during the production pro-
cess. A FourByThree Task Planner then generates a temporally flexible task plan to be
dispatched to the robot through an Executive System (integrated in the ROS-based ar-
chitecture). During the production process, the Executive System is also in charge of
monitoring the plan execution and, in case of need (e.g., a specific command issued
by the human worker), ask the task planner to dynamically face modifications of the
production environment.
4 Conclusions
The dynamic task planning framework briefly described above is to provide the control
architecture with suitable deliberative features relying on the control model generated
by the Knowledge Engineering according to the definition provided by the Produc-
tion Engineer and the preferences of the Human Worker. An off-the-shelf planning and
execution system based on APSI-TRF is then deployed to synthesize a suitable set of
actions (i.e., in this work a timeline-based plan) that when executed controls the mecha-
tronic device.
Acknowledgment. The CNR authors are supported by the European Commission within the
H2020 research and innovation programme, FourByThree project, grant agreement No. 637095.
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