8 papers
Large Language Models for Multi-Robot Systems: A Survey
Peihan Li, Zijian An, Shams Abrar +1
The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, task allocation and planning, and…
LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging
Peihan Li, Joanna Gutierrez, Fabian Hernandez +2
Swarm foraging algorithms, such as the central-place foraging algorithm (CPFA), typically rely on offline parameter optimization using genetic algorithms (GA) or reinforcement lear…
Hierarchical LLMs In-the-Loop Optimization for Real-Time Multi-Robot Target Tracking under Unknown Hazards
Yuwei Wu, Yuezhan Tao, Peihan Li +4
Real-time multi-robot coordination in hazardous and adversarial environments requires fast, reliable adaptation to dynamic threats. While Large Language Models (LLMs) offer strong…
Failure-Aware Multi-Robot Coordination for Resilient and Adaptive Target Tracking
Peihan Li, Jiazhen Liu, Yuwei Wu +1
Multi-robot coordination is crucial for autonomous systems, yet real-world deployments often encounter various failures. These include both temporary and permanent disruptions in s…
Resilient Multi-Robot Target Tracking with Sensing and Communication Danger Zones
Peihan Li, Yuwei Wu, Jiazhen Liu +3
Multi-robot collaboration for target tracking in adversarial environments poses significant challenges, including system failures, dynamic priority shifts, and other unpredictable…
LLM-Flock: Decentralized Multi-Robot Flocking via Large Language Models and Influence-Based Consensus
Peihan Li, Lifeng Zhou
Large Language Models (LLMs) have advanced rapidly in recent years, demonstrating strong capabilities in problem comprehension and reasoning. Inspired by these developments, resear…