Distributed cooperative simultaneous localization and mapping for dense micro-robot swarms: A stigmergic approach with hardware-constrained sensor fusion

Abstract

To address the challenge of deploying dense micro-robot swarms where classical simultaneous localization and mapping (SLAM) methods are computationally infeasible, we propose a hardware-constrained, stigmergic cooperative SLAM framework. Our system enables swarms to map unknown environments in real time, without a central coordinator or high-bandwidth links. Our method introduces five novel components: (i) Stigmergic Counter-Consensus—a bounded, monotone, and bandwidth-frugal consensus rule over occupancy counters; (ii) ATOP-Raycast—an Adaptive Thin-Obstacle-Preserving Bresenham variant with probabilistic endpoint diffusion; (iii) Proximal Delta Encoding of map updates using tilewise run-length and majority masks; (iv) a Budget-Aware extended Kalman filter that codesigns fusion rate and numerical precision with MCU limits; and (v) a Tri-Force Frontier-Cohesion controller yielding emergent exploration while maintaining communication neighborhoods. In real-world validation with 40 robots, the framework achieves a thin-feature retention rate of 92.4% and a final map Intersection-over-Union (IoU) of 0.89. This performance is sustained with a minimal communication overhead of ∼110 bytes per packet, demonstrating near-linear scalability on ESP32-class hardware while preserving critical geometry. We provide algorithmic details, complexity bounds, convergence guarantees, and validate our approach through a comprehensive suite of simulations. Together, these yield near-linear scalability to 40 + robots at 20 Hz on ESP32-class hardware, preserve thin obstacles, and achieve low collision rates with modest communication. We provide algorithmic details, complexity bounds, convergence guarantees, and validate our approach through a comprehensive suite of simulations.