CHORAL: 为安全高效的异构多机器人路由进行路径感知规划
CHORAL: Traversal-Aware Planning for Safe and Efficient Heterogeneous Multi-Robot Routing
AI总结：
CHORAL通过集成语义感知框架，利用开放词汇视觉模型构建度量-语义地图，为异构多机器人提供安全高效的路径规划。
摘要：
在自主机器人中监控大型、未知和复杂环境带来了显著的导航挑战，其中部署具有互补能力的异构机器人团队可以显著提高任务性能和可行性。然而，有效建模不同机器人平台与环境的交互需要丰富的语义场景理解。尽管如此，现有方法往往假设同构机器人团队或专注于离散任务兼容性，而不是连续路由。因此，场景理解没有完全融入路由决策，限制了它们适应环境和利用每个机器人优势的能力。在本文中，我们提出了一种集成的语义感知框架，用于协调异构机器人。从侦察飞行开始，我们使用开放词汇视觉模型构建度量-语义地图，并利用它来识别需要更仔细检查的区域以及为每个平台计算的具备能力的路径。这些随后被纳入一个异构车辆路由公式中，该公式联合分配检查任务并计算机器人轨迹。在模拟和一个包含三个机器人平台的真实检查任务中的实验表明，我们的方法在规划更安全和高效路线方面是有效的，通过明确考虑每个平台的导航能力。我们发布了我们的框架CHORAL作为开源，以支持可重复性和部署多样化机器人团队。
英文摘要：
Monitoring large, unknown, and complex environments with autonomous robots poses significant navigation challenges, where deploying teams of heterogeneous robots with complementary capabilities can substantially improve both mission performance and feasibility. However, effectively modeling how different robotic platforms interact with the environment requires rich, semantic scene understanding. Despite this, existing approaches often assume homogeneous robot teams or focus on discrete task compatibility rather than continuous routing. Consequently, scene understanding is not fully integrated into routing decisions, limiting their ability to adapt to the environment and to leverage each robot's strengths. In this paper, we propose an integrated semantic-aware framework for coordinating heterogeneous robots. Starting from a reconnaissance flight, we build a metric-semantic map using open-vocabulary vision models and use it to identify regions requiring closer inspection and capability-aware paths for each platform to reach them. These are then incorporated into a heterogeneous vehicle routing formulation that jointly assigns inspection tasks and computes robot trajectories. Experiments in simulation and in a real inspection mission with three robotic platforms demonstrate the effectiveness of our approach in planning safer and more efficient routes by explicitly accounting for each platform's navigation capabilities. We release our framework, CHORAL, as open source to support reproducibility and deployment of diverse robot teams.
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。
