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

• URL: http://arxiv.org/abs/2601.10340v1
• Date: Thu, 15 Jan 2026 12:34:22 GMT
• Title: CHORAL: Traversal-Aware Planning for Safe and Efficient Heterogeneous Multi-Robot Routing
• Abstract summary: Monitoring large, unknown, and complex environments with autonomous robots poses significant navigation challenges.<n>We propose an integrated semantic-aware framework for coordinating heterogeneous robots.<n> Experiments in simulation and in a real inspection mission with three robotic platforms demonstrate the effectiveness of our approach.
• Score: 4.3992789649203194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Robots as Tokens: Unified Diffusion Transformer for Coordinated Multi-Robot Trajectory Generation [25.56633710745516]
  We investigate whether coordinated multi-robot trajectories can be learned with a generative model in a feed-forward manner.<n>We propose Robots as Tokens (Roken), a unified diffusion transformer that directly generates multi-robot trajectories.<n>Roken behaves as a versatile multi-robot planner that can handle single-robot planning, coordinated multi-robot trajectory generation, and conditional trajectory generation.
  arXiv  Detail & Related papers  (2026-06-14T02:32:33Z)
• Decentralized Heterogeneous Multi-Robot Collaborative Exploration for Indoor and Outdoor 3D Environments [11.81511178687002]
  This paper proposes a decentralized collaborative framework for heterogeneous multi-robot systems.<n>The framework coordinates exploration tasks among heterogeneous robots, achieving superior exploration efficiency and communication savings.
  arXiv  Detail & Related papers  (2026-04-26T13:16:13Z)
• Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence [54.91177026001217]
  Large language model (LLM)-enabled teaming methods typically assume well-structured and known environments.<n>We present SPINE-HT, a framework that addresses these limitations by grounding the reasoning abilities of LLMs in the context of a heterogeneous robot team.<n>Our framework achieves nearly twice the success rate compared to prior LLM-enabled heterogeneous teaming approaches.
  arXiv  Detail & Related papers  (2025-10-30T18:24:38Z)
• RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation [15.548637925166986]
  We present a multi-domain framework for training, evaluating and deploying RL-based navigation policies across diverse robotic platforms and operational environments.<n>Our work presents four key contributions: (1) a scalable and modular framework, facilitating seamless robot-task interchangeability and reproducible training pipelines; (2) sim-to-real transfer demonstrated through real-world experiments with multiple robots; and (3) the release of the first open-source API for deploying Isaac Lab-trained policies to real robots.
  arXiv  Detail & Related papers  (2025-05-20T15:48:23Z)
• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
  Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision. We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
  arXiv  Detail & Related papers  (2024-07-02T21:00:30Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models [8.668211481067457]
  Co-NavGPT is a novel framework that integrates a Vision Language Model (VLM) as a global planner.<n>Co-NavGPT aggregates sub-maps from multiple robots with diverse viewpoints into a unified global map.<n>The VLM uses this information to assign frontiers across the robots, facilitating coordinated and efficient exploration.
  arXiv  Detail & Related papers  (2023-10-11T23:17:43Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Simultaneous Navigation and Construction Benchmarking Environments [73.0706832393065]
  We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design. In this task, a major robot vision and learning challenge is how to exactly achieve the design without GPS. We benchmark the performance of a handcrafted policy with basic localization and planning, and state-of-the-art deep reinforcement learning methods.
  arXiv  Detail & Related papers  (2021-03-31T00:05:54Z)
• Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms [60.59764170868101]
  Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. We formulate it as a few-shot meta-learning problem where the goal is to find a model that captures the common structure shared across different robotic platforms. We experimentally evaluate our framework on a simulated reaching and a real-robot picking task using 400 simulated robots.
  arXiv  Detail & Related papers  (2021-03-05T14:16:20Z)
• Towards Multi-Robot Task-Motion Planning for Navigation in Belief Space [1.4824891788575418]
  We present an integrated multi-robot task-motion planning framework for navigation in knowledge-intensive domains. In particular, we consider a distributed multi-robot setting incorporating mutual observations between the robots. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning.
  arXiv  Detail & Related papers  (2020-10-01T06:45:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.