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 robotsoperating in large, unknown areas. These environments present unique challengesthat compound the difficulties faced by a robot operator. The use ofmulti-robot teams, assisted by carefully designed autonomy, help reduceoperator workload and allow the operator to effectively coordinate robotcapabilities. In this work, we present a system architecture designed tooptimize both robot autonomy and the operator experience in multi-robotscenarios. Drawing on lessons learned from our team's participation in theDARPA SubT Challenge, our architecture emphasizes modularity andinteroperability. We empower the operator by allowing for adjustable levels ofautonomy ("sliding mode autonomy"). We enhance the operator experience by usingintuitive, adaptive interfaces that suggest context-aware actions to simplifycontrol. Finally, we describe how the proposed architecture enables streamlineddevelopment of new capabilities for effective deployment of robot autonomy inthe field.