去中心化异构多机器人协作探索室内和室外3D环境
Decentralized Heterogeneous Multi-Robot Collaborative Exploration for Indoor and Outdoor 3D Environments
AI总结：
本文提出去中心化协作框架，通过改进的监督体素分割和遗传算法优化，实现异构多机器人在复杂3D环境中的高效探索与通信节约。
摘要：
异构多机器人系统在复杂环境中具有显著适应性。然而，有效协作以充分利用机器人潜力仍是核心挑战。本文提出一种去中心化协作框架，用于自主探索室内和室外3D环境。首先设计了集地形和观测指标于一体的基感知地图。改进的监督体素分割用于简化地图结构并形成支持轻量通信的高层表示。其次，对异构机器人遍历和观测能力进行建模，以评估从不完整监督体素导出的任务视图需求。这些任务视图按需求分组并聚类以简化分配。随后，将视图-聚类分配建模为包含视图-聚类需求与机器人能力之间约束的异构多仓库多旅行商问题（HMDMTSP）。开发了改进的遗传算法以高效求解此问题并确保全局一致性。基于分配，消除集群内的冗余视图以优化探索路线。最后，解决机器人运动路径之间的冲突。在杂乱的室内和室外环境中进行的模拟和实地实验表明，我们的方法有效协调异构机器人探索任务，实现了优于现有方法的探索效率和通信节约。
英文摘要：
Heterogeneous multi-robot systems feature significant adaptability for complex environments. However, effective collaboration that fully exploits the robots' potential remains a core challenge. This paper proposes a decentralized collaborative framework for heterogeneous multi-robot systems to autonomously explore indoor and outdoor 3D environments. First, a basic perception map that integrates terrain and observation metrics is designed. Improved supervoxel segmentation is developed to simplify the map structure and form a high-level representation that supports lightweight communication. Second, the traversal and observation capabilities of heterogeneous robots are modeled to evaluate the requirements of task views derived from incomplete supervoxels. These task views are grouped by requirements and clustered to streamline assignment. Subsequently, the view-cluster assignment is formulated as a heterogeneous multi-depot multi-traveling salesman problem (HMDMTSP) that incorporates constraints between view-cluster requirements and robot capabilities. An improved genetic algorithm is developed to efficiently solve this problem while ensuring global consistency. Based on the assignments, redundant views within clusters are eliminated to refine exploration routes. Finally, conflicts between robots' motion paths are resolved. Simulations and field experiments in cluttered indoor and outdoor environments demonstrate that our approach effectively coordinates exploration tasks among heterogeneous robots, achieving superior exploration efficiency and communication savings compared to state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
