Reducing Uncertainty in Collective Perception using Self-organized Hierarchy

<p>This dataset accompanies an article submission and a&nbsp;<a href="https://github.com/BlueDiamond07/Collective_perception">code repository</a>.</p> <p><strong>Abstract:</strong><br> In collective perception, agents sample spatial data and use the samples to agree on some estimate.&nbsp;In this research, we identify the sources of statistical uncertainty that occur in collective perception and note that improving the accuracy of fully decentralized approaches, beyond a certain threshold, might be intractable.&nbsp;We propose self-organized hierarchy as an approach to improve accuracy in collective perception, by reducing or eliminating some of the sources of uncertainty.&nbsp;Using self-organized hierarchy, aspects of centralization and decentralization can be combined: robots can understand their relative positions system-wide and fuse their information at one point, without requiring, e.g., a fully connected or static communication network.&nbsp;In this way, multi-sensor fusion techniques that have been designed for fully centralized systems can be applied to a self-organized system for the first time, without losing the key practical benefits of decentralization.&nbsp;We implement simple proof-of-concept fusion in a self-organized hierarchy approach and test it against three fully decentralized benchmark approaches. We test the perceptual accuracy of the approaches for time-invariant and time-varying absolute conditions, and test the scalability and fault tolerance of their accuracies.&nbsp;We show that the self-organized hierarchy approach is substantially more accurate, more consistent, and faster than the other approaches, but also that it is comparably scalable and fault-tolerant.</p>