Modular, Resilient, and Scalable System Design Approaches -- Lessons learned in the years after DARPA Subterranean Challenge
<|reference_start|>Modular, Resilient, and Scalable System Design Approaches -- Lessons learned in the years after DARPA Subterranean Challenge: Field robotics applications, such as search and rescue, involve robots operating in large, unknown areas. These environments present unique challenges that compound the difficulties faced by a robot operator. The use of multi-robot teams, assisted by carefully designed autonomy, help reduce operator workload and allow the operator to effectively coordinate robot capabilities. In this work, we present a system architecture designed to optimize both robot autonomy and the operator experience in multi-robot scenarios. Drawing on lessons learned from our team's participation in the DARPA SubT Challenge, our architecture emphasizes modularity and interoperability. We empower the operator by allowing for adjustable levels of autonomy ("sliding mode autonomy"). We enhance the operator experience by using intuitive, adaptive interfaces that suggest context-aware actions to simplify control. Finally, we describe how the proposed architecture enables streamlined development of new capabilities for effective deployment of robot autonomy in the field.<|reference_end|>
@article{sriganesh2024modular,,
    title={Modular, Resilient, and Scalable System Design Approaches -- Lessons
  learned in the years after DARPA Subterranean Challenge},
    author={Prasanna Sriganesh, James Maier, Adam Johnson, Burhanuddin Shirose,
  Rohan Chandrasekar, Charles Noren, Joshua Spisak, Ryan Darnley, Bhaskar
  Vundurthy and Matthew Travers},
    journal={arXiv preprint arXiv:2404.17759},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17759},
    primaryClass={cs.RO}
}