CHORAL: Traversal-Aware Planning for Safe and Efficient Heterogeneous Multi-Robot Routing

2026-01-15

AI Summary

This work addresses the challenge of heterogeneous multi-robot collaborative navigation in complex, unknown environments, where balancing platform-specific capabilities and environmental semantic information remains difficult. The authors propose CHORAL, a novel framework that for the first time deeply integrates open-vocabulary vision models with heterogeneous robot navigation capabilities. CHORAL constructs a metric-semantic map through aerial reconnaissance to identify regions requiring inspection and jointly optimizes capability-aware task allocation with semantic-driven trajectory planning in continuous space. Evaluated in both simulation and real-world experiments involving a team of three heterogeneous robots performing inspection tasks, the approach significantly improves path safety and efficiency, demonstrating its effectiveness. The implementation is open-sourced to facilitate deployment in heterogeneous robotic teams.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• heterogeneous multi-robot routing
• semantic scene understanding
• capability-aware navigation
• autonomous inspection
• traversal-aware planning

Innovation

• semantic-aware planning
• heterogeneous multi-robot routing
• metric-semantic mapping
• open-vocabulary vision
• capability-aware path planning