2 research outputs found
Multi-Agent Consensus Seeking via Large Language Models
Multi-agent systems driven by large language models (LLMs) have shown
promising abilities for solving complex tasks in a collaborative manner. This
work considers a fundamental problem in multi-agent collaboration: consensus
seeking. When multiple agents work together, we are interested in how they can
reach a consensus through inter-agent negotiation. To that end, this work
studies a consensus-seeking task where the state of each agent is a numerical
value and they negotiate with each other to reach a consensus value. It is
revealed that when not explicitly directed on which strategy should be adopted,
the LLM-driven agents primarily use the average strategy for consensus seeking
although they may occasionally use some other strategies. Moreover, this work
analyzes the impact of the agent number, agent personality, and network
topology on the negotiation process. The findings reported in this work can
potentially lay the foundations for understanding the behaviors of LLM-driven
multi-agent systems for solving more complex tasks. Furthermore, LLM-driven
consensus seeking is applied to a multi-robot aggregation task. This
application demonstrates the potential of LLM-driven agents to achieve
zero-shot autonomous planning for multi-robot collaboration tasks. Project
website: westlakeintelligentrobotics.github.io/ConsensusLLM/
Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation
Vision-based cooperative motion estimation is an important problem for many
multi-robot systems such as cooperative aerial target pursuit. This problem can
be formulated as bearing-only cooperative motion estimation, where the visual
measurement is modeled as a bearing vector pointing from the camera to the
target. The conventional approaches for bearing-only cooperative estimation are
mainly based on the framework distributed Kalman filtering (DKF). In this
paper, we propose a new optimal bearing-only cooperative estimation algorithm,
named spatial-temporal triangulation, based on the method of distributed
recursive least squares, which provides a more flexible framework for designing
distributed estimators than DKF. The design of the algorithm fully incorporates
all the available information and the specific triangulation geometric
constraint. As a result, the algorithm has superior estimation performance than
the state-of-the-art DKF algorithms in terms of both accuracy and convergence
speed as verified by numerical simulation. We rigorously prove the exponential
convergence of the proposed algorithm. Moreover, to verify the effectiveness of
the proposed algorithm under practical challenging conditions, we develop a
vision-based cooperative aerial target pursuit system, which is the first of
such fully autonomous systems so far to the best of our knowledge