LATIDIA · Robótica

CoAdapt: un marco basado en LLM para la percepción colaborativa adaptativa en enjambres robóticos IIoT

arXiv: 2609.16852v1Tipo de anuncio: cross Resumen: Los entornos industriales de IoT despliegan cada vez más robots móviles autónomos para tareas como el manejo de materiales, el ensamblaje de productos o la inspección de infraestructuras. En tal caso,

La noticia

arXiv:2609.16852v1 Announce Type: cross 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