Pogobot: an Open-Source, Low-Cost Robot for Swarm Robotics and Programmable Active Matter

TL;DR

Pogobot addresses the need for a low‑cost, accessible platform for swarm robotics and programmable active matter by delivering an open‑source, open‑hardware robot with modular hardware, fast directional infrared communication, and a flexible locomotion suite. The authors present a complete ecosystem including hardware (Head/Belly PCBs, FPGA with a RISC‑V core), software (Pogobios, PogoAPI), and tools (PogoTracker, Pogosim) plus scalable extensions (Pogoremote, Pogowall, Pogocharger, Pogobject). They demonstrate two locomotion modes and quantify communication performance under varying densities, introducing the Banshee protocol and an optional handshake to improve reliability in swarms. The platform’s open nature, along with extensive simulation and tracking tools, supports rapid prototyping and large‑scale experiments in self‑organization, diffusion, and programmable active matter, with adoption across multiple universities. This work lowers barriers to entry for researchers while enabling rigorous multi‑robot experimentation and model validation in PAM contexts.

Abstract

This paper describes the Pogobot, an open-source platform specifically designed for research at the interface of swarm robotics and active matter. Pogobot features vibration-based or wheel-based locomotion, fast infrared communication, and an array of sensors in a cost-effective package (approx. 250euros/unit). The platform's modular design, comprehensive API, and extensible architecture facilitate the implementation of swarm intelligence algorithms and collective motion. Pogobots offer an accessible alternative to existing platforms while providing advanced capabilities including directional communication between units and fast locomotion, all with a compact form factor. More than 200 Pogobots are already being used on a daily basis in several Universities to study self-organizing systems, programmable active matter, discrete reaction-diffusion-advection systems and computational models of social learning and evolution. This paper details the hardware and software architecture, communication protocols, locomotion mechanisms, and the infrastructure built around the Pogobots.

Figures (17)

• Figure 1: From top-left to bottom-right: (a) a small swarm of $8$ Pogobots; (b) Pogobot with wheel-based locomotion; Pogobot with vibration-induced locomotion, using (c) TPU 3D-printed brushes or (d) or commercial toothbrushes head with or without inclined brush ; (e) view from above (the Pogobot is $\sim$6 cm diameter).
• Figure 2: (a) Schematic representation of the Pogobot showing its main components. (b) Close-up of the Head board.
• Figure 3: Pogobot software and hardware stack. Each robot combines three levels: user program, API (PogoLib/PogoAPI), and hardware. Robot-to-robot interactions is performed through IR communication.
• Figure 4: (a) Snapshot of an experiment in real conditions, with extracted position and orientation of all robots. Inset: a top view of the Hat used for tracking. (b) Example of trajectories for each locomotion modality (red: wheels; blue: 3d-printed TPU legs; green: commercial toothbrush legs) (c) zoom on the trajectory obtained with the toothbrush.
• Figure 5: Characterization of the dynamics observed for the three locomotion modalities reported above: (a) speed of the center, (b) angular frequency of the orientation and (c) radius of curvature obtained for increasing values of PWM.