Productive Multitasking for Industrial Robots
This addresses the problem of setup time constraints in industrial automation for small-batch production, representing an incremental improvement in multitasking methods.
The paper tackles the challenge of enabling industrial robots to efficiently handle small-batch production by allowing multiple concurrent tasks without waiting for full completion, and it demonstrates benefits through extensive trials in the ARIAC environment.
The application of robotic solutions to small-batch production is challenging: economical constraints tend to dramatically limit the time for setting up new batches. Organizing robot tasks into modular software components, called skills, and allowing the assignment of multiple concurrent tasks to a single robot is potentially game-changing. However, due to cycle time constraints, it may be necessary for a skill to take over without waiting on another to terminate, and the available literature lacks a systematic approach in this case. In the present article, we fill the gap by (a) establishing the specifications of skills that can be sequenced with partial executions, (b) proposing an implementation based on the combination of finite-state machines and behavior trees, and (c) demonstrating the benefits of such skills through extensive trials in the environment of ARIAC (Agile Robotics for Industrial Automation Competition).