Modular, Resilient, and Scalable System Design Approaches -- Lessons learned in the years after DARPA Subterranean Challenge

TL;DR

This paper presents a modular, resilient system architecture for multi-robot search and rescue operations, emphasizing autonomy, operator experience, and adaptability, based on lessons learned from the DARPA Subterranean Challenge.

Contribution

It introduces a flexible, interoperable architecture with adjustable autonomy levels and adaptive interfaces, improving field deployment and operator control in complex environments.

Findings

Enhanced operator control with sliding autonomy modes

Improved multi-robot coordination in unknown terrains

Streamlined development of new autonomous capabilities

Abstract

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.