The increasing adoption of robotic applications collaborating with
humans within industrial (e.g. manufacturing, intra-logistics) and
civil (e.g. hospital, pharmaceutical) environments is demanding for
the development of new algorithms, extending state-of-the-art AI
techniques to cope with the new competing requirements: safety for the
interaction with humans; speed to improve productivity.
Artificial Intelligence operational planning, scheduling and actuation
(motion planning) of advanced robotic platforms (AGV, UAV, ...) are
the mean to orchestrate the robotic operations in collaborating with
human to achieve complex business relevant objectives like e.g. reduce
time-span, optimize resource consumption, quickly adapt to
contingencies (e.g. product and/or platform faults, changes in
objectives, changes in human operations). As the capabilities
(e.g. dexterity) of robotic agents grows and their adoption in solving
complex tasks collaborating with humans increases together with an
increase in the complexity of the objectives, the problems of finding
feasible plans to orchestrate the different agents, and of dynamically
adapt and react to run-time contingencies to fulfill the high level
objectives are becoming more and more difficult.
The main tasks for the PhD student are to work on algorithms and
artificial intelligence techniques from the fields of constraint
programming, mathematical optimization, path planning, evolutionary
algorithms and/or reinforcement learning i) to solve such planning,
scheduling, actuation (motion planning) and reactive adaptation
problems; ii) to deepen our understanding of these techniques; iii)
and to show how they can be successfully combined to solve problems of
industrial and civil relevance. Moreover, the PhD student will be
allowed to experiment the developed techniques and algorithms in
realistic-size facilities (e.g. Robotic Labs at the University, and
industrial facilities joint with the University of Trento) equipped
with state-of-the-art robotic and automation platforms.
The PhD student will also experiment his/her results in realistic
industrial environments identified in SMEs located in the internal or
marginalized areas of Italy.
