基于任务驱动地图压缩的有袋式地空机器人团队协同探索
Collaborative Exploration with a Marsupial Ground-Aerial Robot Team through Task-Driven Map Compression
AI总结：
针对通信受限环境下的大规模未知区域探索，提出有袋式地空机器人协同框架，采用基于图的路径规划与任务驱动地图压缩，在仿真和真实实验中提升了探索效率并降低了通信开销。
摘要：
未知环境的高效探索对于自主机器人至关重要，尤其是在通信受限的封闭和大规模场景中。为应对这一挑战，我们提出了一种针对有袋式地空机器人团队的协同探索框架，该框架充分利用了两种平台的互补能力。该框架采用基于图的路径规划算法来引导探索，并在预期收益显著超过地面机器人的区域（如大型开放空间或地面平台无法到达的区域）部署空中机器人，从而最大化覆盖范围和效率。为促进大规模空间信息共享，我们引入了一种带宽高效、任务驱动的地图压缩策略。该方法使每个机器人能够在高压缩率下重建分辨率特定的体素地图，同时保留对探索至关重要的细节。通过选择性压缩和共享关键数据，通信开销被最小化，确保了协同路径规划的有效地图集成。仿真和真实世界实验验证了所提方法的有效性，表明其在提高探索效率的同时显著减少了数据传输。
英文摘要：
Efficient exploration of unknown environments is crucial for autonomous robots, especially in confined and large-scale scenarios with limited communication. To address this challenge, we propose a collaborative exploration framework for a marsupial ground-aerial robot team that leverages the complementary capabilities of both platforms. The framework employs a graph-based path planning algorithm to guide exploration and deploy the aerial robot in areas where its expected gain significantly exceeds that of the ground robot, such as large open spaces or regions inaccessible to the ground platform, thereby maximizing coverage and efficiency. To facilitate large-scale spatial information sharing, we introduce a bandwidth-efficient, task-driven map compression strategy. This method enables each robot to reconstruct resolution-specific volumetric maps while preserving exploration-critical details, even at high compression rates. By selectively compressing and sharing key data, communication overhead is minimized, ensuring effective map integration for collaborative path planning. Simulation and real-world experiments validate the proposed approach, demonstrating its effectiveness in improving exploration efficiency while significantly reducing data transmission.
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