Integrated Topological Planning
and Scheduling for Orchestrating Large
Human-Robot Collaborative Teams
Ioannis Chatzikonstantinou
(B) , Ioannis Kostavelis , Dimitrios Giakoumis ,
and Dimitrios Tzovaras
Centre for Research and Technology Hellas, Information Technologies Institute,
6th km Harilaou - Thermi, 57001 Thessaloniki, Greece
{ihatz,gkostave,dgiakoum,Dimitrios.Tzovaras}@iti.gr
https://www.certh.gr
Abstract. Human-Robot Collaboration (HRC) in industry is a promising research direction that has potential to expand robotics to previously
unthinkable application areas. Orchestration of large hybrid humanrobot teams carrying out many tasks in parallel within a shop floor
faces new challenges due to unique aspects introduced by HRC. This
paper presents a new approach to topological and temporal orchestration of hybrid human-robot workforce, considering the capabilities of
robot agents as well as the potentially new roles that human operators may acquire in an HRC setting. We propose a two-stage approach
to orchestrating large HRC teams: First, an abstract topological and
task assignment problem is solved, which does not consider the precise
sequence of tasks. Second, the result of the first step is used to initialize
a constrained search for an efficient HRC schedule. Initial application of
the proposed approach in problems of varying complexity demonstrates
encouraging results.
Keywords: Human-robot collaboration · Scheduling · Topology ·
Optimization · Industry 4.0 · Waste electrical and electronic
equipment · Recycling
1 Introduction
Automation of industrial processes through the use of robots is a widely practiced
approach in modern industry. As the paradigm in industry shifts to Industry 4.0,
new, previously unexplored areas of robotics applications in industry emerge as
novel research directions. One such area concerns collaborative work performed
in an industrial environment by hybrid human-robot teams. Human-robot collaboration (HRC) is necessary to efficiently address the more challenging tasks
This work has been supported by the European Union Horizon 2020 Research and
Innovation program “HR-Recycler” under Grant Agreement no. 820742.
c
Springer Nature Switzerland AG 2020
V. Vouloutsi et al. (Eds.): Living Machines 2020, LNAI 12413, pp. 23–35, 2020.
https://doi.org/10.1007/978-3-030-64313-3_4
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