Authors:
Ana Macedo
;
Liliana Antão
;
João Reis
and
Gil Gonçalves
Affiliation:
Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Keyword(s):
Collaborative Robotics, Task Allocation, Scheduling, Task Graph, Reinforcement Learning.
Abstract:
Collaborative robots are increasingly used in assembly processes, particularly in teams (Human-Robot or Robot-Robot), expanding the complexity and possible alternative sequences of operation and ways of team allocation to complete the assembling of a product. With this complexity, representing the possible sequences of actions needed to complete the task and the necessary constraints in a graph would improve the flexibility provided by team collaboration. However, the best sequence must be selected to increase productivity and profit, which is still challenging for a robot. This work proposes a modular system composed of three different components that, in a closed-loop interaction, allows a robotic agent to correctly plan a task given a set of operations, and optimize the task sequence allocation and scheduling plan. The effectiveness of the system is evaluated within an assembly process of different types of furniture for task sequence and allocation. The agent was able to converge
successfully in three assembly scenarios: a table with 1 leg, a table with 2 legs and a table with 4 legs. Moreover, in the task allocation tests, the robotic agent was able to select actions according to the human operator expertise and its impact in the task completion time.
(More)