Summary
Robots working together in factories need to share information to better understand their surroundings. The authors created a system called CoAdapt that uses a large language model to decide which robots should share data and how they should combine it, adapting to changing positions and network conditions. This method reduces the amount of data sent by over a third without losing the accuracy of what the robots detect. It works by understanding descriptions of the robot scene generated from sensor data, without requiring special training. The system was tested in many simulated situations and proved to be more communication-efficient than earlier methods.
What this means in practice
- •For factory automation teams: Optimize data sharing among mobile robots to keep sensing accurate while reducing network load in dynamic industrial environments.
- •For logistics robotics developers: Improve robot swarm communication strategies to adapt in real time to changing robot positions and connectivity in warehouses.
Authors
Houssam Hajj Hassan, Antonia Maria Masucci, Lynda Zitoune, Salah-Eddine Elayoubi
Abstract
Industrial IoT environments increasingly deploy autonomous mobile robots for tasks such as material handling, product assembly, or infrastructure inspection. In such deployments, collaborative perception enables robots to share LiDAR observations and collectively construct a richer model of their environment than an individual agent could produce alone. However, industrial environments are dynamic spaces where robot positions shift continuously, network bandwidth fluctuates, and the marginal contribution of robots to perception quality varies at runtime. Existing collaborative perception approaches are designed for static participation assumptions and cannot adapt to these dynamics without sacrificing either detection precision or communication efficiency. This paper presents CoAdapt, an adaptive collaborative perception framework for IIoT robotic swarms in which a Large Language Model (LLM) serves as a runtime fusion controller, jointly deciding which robots participate in the fusion process and which fusion algorithm to apply based on the current spatial configuration and network state. The LLM reasons over structured natural language descriptions of the scene derived from raw LiDAR point clouds, requiring no taskspecific training and generalizing to unseen swarm topologies. Evaluated on the OPV2V benchmark across 25 scenarios, our approach achieves a 38% reduction in communication cost while maintaining detection precision comparable to static baseline approaches.