CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms

• URL: http://arxiv.org/abs/2609.16852v1
• Date: Tue, 15 Sep 2026 08:44:08 GMT
• Title: CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms
• Abstract summary: CoAdapt is an adaptive collaborative perception framework for IIoT robotic swarms.<n>Our approach achieves a 38% reduction in communication cost while maintaining detection precision comparable to static baseline approaches.
• Score: 2.6155698073666023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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