How can LLMs predict robot arrival times in complex environments?
CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots
This paper introduces CAMETA, a framework for predicting the arrival times (ETAs) of multiple robots navigating an unstructured environment. It combines path planning with a graph neural network (GNN) to model potential conflicts and improve ETA accuracy.
Relevant to LLM-based multi-agent systems are the use of a heterogeneous graph to represent agent interactions and environment structure, a GNN for processing this graph to forecast agent behavior (ETA prediction), and the potential for adapting this approach to model more complex agent interactions and dynamics beyond simple robot navigation. The paper emphasizes the importance of conflict prediction and robust performance in noisy, real-world environments where agent actions might deviate from planned paths, similar to the uncertainties faced in LLM-based agent interactions.