1 citations · 4 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
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…