  Industry 4.0 proposes the integration of artificial intelligence (AI) into
manufacturing and other industries to create smart collaborative systems which
enhance efficiency. The aim of this paper is to develop a flexible and adaptive
framework to generate optimal plans for collaborative robots and human workers
to replace rigid, hard-coded production line plans in industrial scenarios.
This will be achieved by integrating the Planning Domain Definition Language
(PDDL), Partial Order Planning Forwards (POPF) task planner, and a task
allocation algorithm. The task allocation algorithm proposed in this paper
generates a cost function for general actions in the industrial scenario, such
as PICK, PLACE, and MOVE, by considering practical factors such as feasibility,
reachability, safety, and cooperation level for both robots and human agents.
The actions and costs will then be translated into a language understandable by
the planning system using PDDL and fed into POPF solver to generate an optimal
action plan. In the end, experiments are conducted where assembly tasks are
executed by a collaborative system with two manipulators and a human worker to
test the feasibility of the theory proposed in this paper.
