MOSAIC: 異種ロボットチームの知的連携のためのモジュール式スケーラブル自律性
MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams
単一オペレータの監督下で異種ロボットチームが科学探査を行うためのスケーラブルな自律性フレームワークを提案し、月面探査模擬実験で1台故障時でもタスクの82.3%を完了した。
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著者: David Oberacker, Julia Richter, Philip Arm, Marvin Grosse Besselmann, Lennart Puck, William Talbot, Maximilian Schik, Sabine Bellmann, Tristan Schnell, Hendrik Kolvenbach, Rüdiger Dillmann, Marco Hutter, Arne Roennau
分類: cs.RO
原文アブストラクト
Mobile robots have become indispensable for exploring hostile environments, such as in space or disaster relief scenarios, but often remain limited to teleoperation by a human operator. This restricts the deployment scale and requires near-continuous low-latency communication between the operator and the robot. We present MOSAIC: a scalable autonomy framework for multi-robot scientific exploration using a unified mission abstraction based on Points of Interest (POIs) and multiple layers of autonomy, enabling supervision by a single operator. The framework dynamically allocates exploration and measurement tasks based on each robot's capabilities, leveraging team-level redundancy and specialization to enable continuous operation. We validated the framework in a space-analog field experiment emulating a lunar prospecting scenario, involving a heterogeneous team of five robots and a single operator. Despite the complete failure of one robot during the mission, the team completed 82.3% of assigned tasks at an Autonomy Ratio of 86%, while the operator workload remained at only 78.2%. These results demonstrate that the proposed framework enables robust, scalable multi-robot scientific exploration with limited operator intervention. We further derive practical lessons learned in robot interoperability, networking architecture, team composition, and operator workload management to inform future multi-robot exploration missions.
