Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming

2025-10-15

AI Summary

This paper addresses the multi-agent task allocation and planning (MATP) problem for heterogeneous robot teams in obstacle-dense environments. To tackle this challenge, we propose the OATH framework, which comprises three core components: (1) an obstacle-aware adaptive Halton sequence sampling strategy to enhance environmental coverage quality; (2) a three-tier collaborative “clustering–auction–selection” mechanism integrating weighted auction with intra-cluster task prioritization; and (3) real-time natural language instruction parsing via large language models (LLMs) to enable semantic-driven dynamic re-planning. Extensive experiments conducted on the NVIDIA Isaac Sim platform demonstrate that OATH significantly outperforms state-of-the-art approaches across multiple metrics—particularly in task allocation quality, system scalability, obstacle adaptability, and overall execution efficiency. The framework proves especially effective in highly dynamic, constraint-intensive complex scenarios.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Multiagent Planning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Multi-Agent Task Assignment and Planning (MATP) has attracted growing attention but remains challenging in terms of scalability, spatial reasoning, and adaptability in obstacle-rich environments. To address these challenges, we propose OATH: Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming, which advances MATP by introducing a novel obstacle-aware strategy for task assignment. First, we develop an adaptive Halton sequence map, the first known application of Halton sampling with obstacle-aware adaptation in MATP, which adjusts sampling density based on obstacle distribution. Second, we propose a cluster-auction-selection framework that integrates obstacle-aware clustering with weighted auctions and intra-cluster task selection. These mechanisms jointly enable effective coordination among heterogeneous robots while maintaining scalability and near-optimal allocation performance. In addition, our framework leverages an LLM to interpret human instructions and directly guide the planner in real time. We validate OATH in NVIDIA Isaac Sim, showing substantial improvements in task assignment quality, scalability, adaptability to dynamic changes, and overall execution performance compared to state-of-the-art MATP baselines. A project website is available at https://llm-oath.github.io/.

Problem

• Addresses scalability and adaptability in multi-robot task planning
• Introduces obstacle-aware strategies for heterogeneous robot coordination
• Enables real-time human instruction interpretation using LLM guidance

Innovation

• Adaptive Halton sampling adjusts density for obstacles
• Cluster-auction-selection framework integrates obstacle-aware coordination
• LLM interprets human instructions to guide planner