Lightweight change detection improves swarm robots in dynamic environments
Lightweight Change Detection for Adaptive Collective Perception in Dynamic Environments
Robotics
Summary
Swarm robots work together to explore and understand their surroundings using shared information. This study looks at how these robots can better detect when their environment changes over time, especially in settings where patterns and proportions shift. The authors introduce a simple way for robots with limited memory and processing power to notice changes quickly and reset their thinking to stay accurate. Their method also adapts how often robots take samples to avoid mistakes from repeated similar readings. Simulations show their approach helps robots make smarter decisions even when the environment is always shifting.
What this means in practice
- •For robotics engineers: Implement efficient change detection for robots exploring changing environments with limited memory and processing power.
- •For industrial inspection teams: Use adaptive swarm perception methods to maintain accurate condition monitoring in environments with shifting surface patterns.
Authors
Thiemen Siemensma, Tiago Travassos, Jiacheng Zhang, Bahar Haghighat
Abstract
Swarm robotics enables scalable inspection through collective perception, where robots infer environmental properties from distributed observations. A common benchmark considers robots exploring black-white tiled environments and determining the majority tile type. While most work studies static environments, dynamic environments remain underexplored. We study collective perception in dynamic environments where the tile proportion and the spatial correlation of surface patterns may change over time. To enable implementation on resource-constrained robots, we integrate a lightweight change detection mechanism into a Bayesian collective decision-making framework that resets robot beliefs when distribution shifts are detected. The proposed detector requires constant memory and constant per-sample computation, unlike two recent competitive approaches that store the full sample stream, making it suitable for memory-constrained robots. We further introduce a method that detects spatially correlated sampling and adapts sampling intervals online to mitigate correlation effects. Our simulation results in Webots show competitive change detection delays, more consistent precision across communication settings, and improved decision accuracy under changing spatial correlations.