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Graphical State-Space Programmability as a Natural Interface for Robotic Control

This paper explores Graphical State-Space Programming (GSSP) as an intuitive interface for robotic control, focusing on its application in underwater robots for marine ecosystem health monitoring. GSSP allows users to specify tasks visually, such as taking pictures of corals or searching for specific fish, using a simple GUI without heavy coding complexities. The study compares GSSP with traditional textual programming, demonstrating that it enhances user experience, reduces programming errors, and accelerates development. Key findings highlight GSSP's effectiveness in achieving quicker task execution and minimizing learning overhead for users.

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Graphical State-Space Programmability as a Natural Interface for Robotic Control

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  1. Graphical State-Space Programmability as a Natural Interface for Robotic Control Anqi Xu, Gabriel Charette, Junaed Sattar, Gregory Dudek McGill University, Canada Thursday, May 6th, 2010

  2. Overview • Existing robot programming techniques • Graphical State-Space Programming (GSSP) • Implementation on a representative robotic platform • User Study Anqi Xu, McGill University

  3. Aqua: A Family of Underwater Robots • Survey health of marine ecosystems • Location-specific tasks: • “Take picture of this coral” • “Search for that type of fish” • Infrastructure representative of most robotic platforms Anqi Xu, McGill University

  4. Robot Programming Interfaces • Textual programming Anqi Xu, McGill University

  5. Robot Programming Interfaces (cont.) • Textual programming • Data flow programming (e.g. visual front-end for code) Lego® Mindstorms® NXT-G Software Anqi Xu, McGill University

  6. Robot Programming Interfaces (cont.) • Textual programming • Data flow programming (e.g. visual front-end for code) • Graphical control / tele-operation Procerus® Virtual Cockpit™ Software Anqi Xu, McGill University

  7. Task Specification • Textual & data flow programming: if-then-else / switch • Full control over sequence of execution • Ability to specify branching paths • Nested clauses hard to debug • Graphical control: on-screen command selection • Commands placed at distinct locations on map • No path branching capabilities • Limited selection of tasks Anqi Xu, McGill University

  8. Graphical State-Space Programming (GSSP) • Insert waypoints through GUI • Location-specific tasks: standard computer code attached to waypoints • Branching & looping paths • Natural visualization of data flow • Core robot AI code decoupled from path itineraries • Minimal overlap of functionalities • High-level robot interaction Anqi Xu, McGill University

  9. GSSP Implementation onboard Aqua • Existing development process • Long compilation times • Monolithic codebase • Limited on-board processing capabilities • Revamped development process • Scripts interpreted at runtime using Python API wrapper • Modular client-server design • Upgradable client-side processing capabilities Anqi Xu, McGill University

  10. Path Generator GUI • Location-specific Python scripts • Load new paths within scripts • alternating routes • contingency plans • etc. • Create / modify paths at runtime • based on current state & latest observations Anqi Xu, McGill University

  11. User Study • Graphical state-space programming versus textual scripting • Trajectory through all marked locations • Each icon corresponds to a specific action command • For textual scripting, use additional function calls to move robot • 5 subjects, 4 sessions • Alternate between GSSP & textual interfaces • Evaluation metric: elapsed duration Anqi Xu, McGill University

  12. Study Results • GSSP interface consistently faster than procedural coding • GSSP has minimal learning overhead • GSSP’s high-level interaction prevents path-related & other simple programming errors Anqi Xu, McGill University

  13. Conclusion • Graphical state-space programming paradigm • Geographicalvisualization of location-specific commands • Code for core AI routines decoupled from path itineraries • Advantages: • Smaller codebase • Faster development time • High-level robot interaction • Ongoing work: • Provide simultaneous visualization of branching paths • Integrate with other robotic platforms Anqi Xu, McGill University

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