AutoPercep: A Pipeline for Onboard Neighbor Position Estimation Toward Large-Scale Swarm Robotics

Abstract

Autonomous mobile robots must know each other's positions to coordinate their actions and motion. Beyond collision avoidance, relative position estimation is essential for spatial coordination tasks such as collective motion, leader–follower dynamics, or formation control.To overcome the scalability and resilience issues of centralized orchestrators that transmit real-time positional information to every robot, we study mechanisms of onboard vision sensing. Conventional localization methods, such as SLAM, are typically too computationally demanding for real-time use on small, resource-constrained mobile robots. Vision-based neural networks offer a promising alternative but often require large, high-quality datasets that are expensive to collect. We present AutoPercep, a~pipeline that automatically generates training data and trains a lightweight neural network to estimate neighbor positions. Robots capture camera images that are automatically labeled using ground-truth data from a motion-capture system. In our experiments, AutoPercep collected over 10,000 high-quality images within 10 minutes and trained a neural network in about 1 hour, which could be deployed on Raspberry Pi 4B–based robots for onboard neighbour detection. Moreover, we show that a network trained on five robots generalizes to seven-robot deployments. We finally evaluate the trained model in a sequential leader-follower case study. Our end-to-end pipeline demonstrates the feasibility and low cost of onboard, vision-based neighbor perception, supporting scalability to large robot swarms and opening opportunities for deployment beyond laboratory settings. The code for training and evaluation is available at https://github.com/preon7/autopercep