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.

翻译：现场机器人应用，如搜索与救援，涉及机器人在广阔未知区域中运行。此类环境对操作员构成独特挑战，加剧了操控难度。通过精心设计的自主性辅助多机器人团队，可有效降低操作员负荷，使其高效协调机器人能力。本文提出一种面向多机器人场景的系统架构，旨在优化机器人自主性与操作员体验。基于团队参与DARPA SubT挑战赛的经验积累，该架构强调模块化与互操作性。我们通过可调节自主性层级（"滑动模式自主性"）增强操作员能力，并采用支持情境感知功能的直观自适应界面简化控制以提升操作体验。最后，我们阐述了该架构如何助力高效开发新型功能，实现机器人自主性在实战中的有效部署。