# Lightweight Change Detection for Adaptive Collective Perception in Dynamic Environments

- Source: arXiv cs.RO
- Published: 2026-10-27T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/sayoaswzrxm7dlmr22t1qd6rr
- Original: https://arxiv.org/abs/2610.34059

## Summary

For dynamic environments where tile proportions and the spatial correlation of surface patterns change over time, this work integrates a lightweight change detection mechanism into a Bayesian collective decision-making framework, resetting robot beliefs when distribution shifts are detected. The detector requires only constant memory and constant per-sample computation, unlike two recent methods that must store the full sample stream, making it suitable for memory-constrained robots. Webots simulations show competitive change detection latency, more consistent accuracy across different communication settings, and improved decision accuracy when spatial correlation changes.