让空地一体机器人自主探索，兼顾能耗与时间效率。
Autonomous Exploration with Terrestrial-Aerial Bimodal Vehicles
- 分层框架动态选择空中/地面模式并规划观测点顺序。
- 采用改进的蒙特卡洛树搜索，在有限能耗下提升探索覆盖率。
- 适合需要长时续航与灵活移动的实地探测任务。
空地一体化机器人结合了飞行器的高机动性与地面机器人的长续航优势，具备显著的自主探索潜力。针对实际探索任务中固有的能量与时间约束，本文提出一种分层框架，使该机器人能利用其灵活的运动模态进行探索。首先提取环境信息以识别信息丰富的区域，并生成一组潜在的空地复合视角；为自适应管理能耗与时间约束，引入改进的蒙特卡洛树搜索方法，策略性优化模态选择与视角序列；结合改进的空地一体化运动规划器，构建完整的能耗与时间感知探索系统。大量仿真及在定制化真实平台上的部署结果验证了该系统的有效性。
原文摘要 · Abstract (English)
Terrestrial-aerial bimodal vehicles, which integrate the high mobility of aerial robots with the long endurance of ground robots, offer significant potential for autonomous exploration. Given the inherent energy and time constraints in practical exploration tasks, we present a hierarchical framework for the bimodal vehicle to utilize its flexible locomotion modalities for exploration. Beginning with extracting environmental information to identify informative regions, we generate a set of potential bimodal viewpoints. To adaptively manage energy and time constraints, we introduce an extended Monte Carlo Tree Search approach that strategically optimizes both modality selection and viewpoint sequencing. Combined with an improved bimodal vehicle motion planner, we present a complete bimodal energy- and time-aware exploration system. Extensive simulations and deployment on a customized real-world platform demonstrate the effectiveness of our system.
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