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.

翻译：利用自主机器人监测大型、未知且复杂的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大幅提升任务性能与可行性。然而，有效建模不同机器人平台如何与环境交互需要丰富的语义场景理解。尽管如此，现有方法通常假设机器人团队是同质的，或专注于离散的任务兼容性而非连续的路由规划。因此，场景理解未能完全融入路由决策，限制了其适应环境及发挥各机器人优势的能力。本文提出一种集成的语义感知框架，用于协调异构机器人。从一次侦察飞行开始，我们利用开放词汇视觉模型构建度量-语义地图，并据此识别需要近距离检查的区域，以及为每个平台规划能力感知的路径以抵达这些区域。随后，这些信息被纳入一个异构车辆路由问题中，该问题联合分配检查任务并计算机器人轨迹。在仿真及包含三个机器人平台的实际检查任务中进行的实验表明，我们的方法通过显式考虑每个平台的导航能力，能够规划出更安全、更高效的路径。我们将框架CHORAL开源发布，以支持异构机器人团队的可复现性与实际部署。