Abstract

RobotCat uses a goal-oriented, declarative approach for robot programming that leverages mathematical representations found in category theory as a way to formally model modularity and behavioral knowledge. We define hierarchical interfaces that require only local modifications when new software or work cell models are introduced, thus making robot programming more interoperable. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —RoboCat can be put into practice and be beneficial to different stakeholders: 1) a student (or small, individual manufacturer) is in charge of planning the production of a new product. Instead of tedious programming, she sketches out process diagrams using conceptual templates for actions (e.g., “move,” “carry,” “locate”) that can produce vastly more resilient production plans than would normally be expected from someone without many years of experience; 2) a robot platform manufacturer has a new manipulator that they would like to provide to the existing customers. Usually, such an upgrade requires a complex retooling of the fabrication lines to accommodate the new functionality. Further complicating matters, the new manipulator contains third-party hardware and software, requiring a full re-architecting of the integrated system. Rather than a wholesale swap of the old system for the new, the third-party application programming interfaces (APIs) can be systematically translated to fit snuggly within the existing modular system with seamless interoperability; and 3) a company is designing the production process associated with their new product launch and they wish to spread their supply chain across several manufacturing vendors. Rather than writing individual process plans customized for each manufacturer, they compose a high-level plan in a functional language. This plan is compiled into implementations specific to each manufacturer’s systems, without the need for human intervention.

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