Bugs with Features: Vision-Based Fault-Tolerant Collective Motion Inspired by Nature
Peleg Shefi, Amir Ayali, Gal A. Kaminka

TL;DR
This paper introduces biologically inspired mechanisms to improve the robustness of vision-based collective motion in robotic swarms, addressing perception limitations and fault tolerance to enhance resilience.
Contribution
It presents new methods for distance estimation and intermittent locomotion, significantly improving swarm robustness against perception errors and faulty robots.
Findings
Enhanced swarm resilience in simulations
Effective fault detection despite classification errors
Robustness applicable to various collective motion models
Abstract
In collective motion, perceptually-limited individuals move in an ordered manner, without centralized control. The perception of each individual is highly localized, as is its ability to interact with others. While natural collective motion is robust, most artificial swarms are brittle. This particularly occurs when vision is used as the sensing modality, due to ambiguities and information-loss inherent in visual perception. This paper presents mechanisms for robust collective motion inspired by studies of locusts. First, we develop a robust distance estimation method that combines visually perceived horizontal and vertical sizes of neighbors. Second, we introduce intermittent locomotion as a mechanism that allows robots to reliably detect peers that fail to keep up, and disrupt the motion of the swarm. We show how such faulty robots can be avoided in a manner that is robust to errors…
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Taxonomy
TopicsDistributed Control Multi-Agent Systems · Modular Robots and Swarm Intelligence · Robot Manipulation and Learning
