A Robot Web for Distributed Many-Device Localisation (2202.03314v2)

Abstract: We show that a distributed network of robots or other devices which make measurements of each other can collaborate to globally localise via efficient ad-hoc peer to peer communication. Our Robot Web solution is based on Gaussian Belief Propagation on the fundamental non-linear factor graph describing the probabilistic structure of all of the observations robots make internally or of each other, and is flexible for any type of robot, motion or sensor. We define a simple and efficient communication protocol which can be implemented by the publishing and reading of web pages or other asynchronous communication technologies. We show in simulations with up to 1000 robots interacting in arbitrary patterns that our solution convergently achieves global accuracy as accurate as a centralised non-linear factor graph solver while operating with high distributed efficiency of computation and communication. Via the use of robust factors in GBP, our method is tolerant to a high percentage of faults in sensor measurements or dropped communication packets.

• The paper introduces a novel distributed localisation framework that uses Gaussian Belief Propagation for robust multi-robot positioning.
• It employs a modular factor graph where each robot processes local odometry and peer measurements to maintain network consistency.
• Empirical results validate its efficiency and resilience, achieving global convergence even under high robot density and unreliable communication.

Distributed Multi-Robot Localisation Through Gaussian Belief Propagation

This paper presents an innovative approach to global localisation for multi-robot systems through the development of the "Robot Web." It proposes a distributed network architecture where multiple robots, or other devices, localise themselves and each other by making mutual observations and engaging in peer-to-peer communication using Gaussian Belief Propagation (GBP). The paper outlines a distributed and asynchronous methodology designed to efficiently solve the global localisation problem without the need for centralized coordination, providing a scalable and flexible solution adaptable to various kinds of robots, motions, and sensors.

Framework and Approach

The framework leverages the probabilistic factor graph representation to manage observed data and Gaussian Belief Propagation as the inference method. Each robot maintains a localized segment of the factor graph containing its state variables, odometry factors, and measurements, while inter-robot measurements are processed through GBP on this distributed structure. The modular design allows robots to exchange information through a straightforward interface, even when communication is unreliable and asynchronous, without needing centralized data processing.

The paper emphasizes the computational efficiency and scalability of Robot Web. This setup is beneficial in environments where data privacy or operational independence from centralized infrastructures is necessary. The robots exchange small, structured messages—vectors and matrices—that help in maintaining local graph consistency and propagate necessary information across the distributed network.

Results and Evaluation

The paper rigorously evaluates Robot Web's localization accuracy and robustness against centralized graph optimization solutions, noting convergence patterns that achieve global localization accuracy. Empirical simulations demonstrate successful operation across varied robot densities and noise conditions, and notable scalability is showcased with simulations including up to 1000 robots. Moreover, the robustness to faulty sensor measurements and packet loss is validated through simulations, asserting that GBP can remain operationally viable with even significant computational perturbations.

By applying robust kernels within GBP, the proposed method handles many non-Gaussian outlier measurements effectively, thus ensuring strong adaptability to real-world moderation where sensor data inaccuracies are prevalent. A key contribution is tackling the conventional challenges in distributed localisation, facilitating the integration of newly introduced robots to the web with minimal setup requirements.

Implications and Future Directions

This research advances multi-robot systems by eliminating dependences on centralized processing—encouraging autonomy and cooperative behavior among heterogeneous devices through standardized communication paradigms. This flexibility presents significant implications for fields ranging from autonomous vehicle coordination to optimized logistics management and distributed sensor networks.

Future explorations might involve extending GBP to more complex parameterizations and states, optimizing long-term operation without losing efficiency, and exploring integration with existing multi-robot frameworks. Practical implementations could focus on improving initialisation processes, dynamically adjusting communication range and protocols based on environmental factors, and integrating additional sensory inputs without substantial structural changes.

By promoting a distributed, open communication model, Robot Web aligns with the potential future of AI applications in collective environments. It provides promising insights into creating scalable, robust solutions for real-time, distributed spatial awareness, paving the way for collaborations and innovation in the artificial intelligence domain.