Bugs with Features: Vision-Based Fault-Tolerant Collective Motion Inspired by Nature

2025-12-26

AI Summary

Visual perception-based artificial bee swarms suffer from poor robustness due to ambiguous local sensing, information loss, and cascading collective failure triggered by individual faults. Method: Inspired by locust swarm dynamics, this work proposes a fault-tolerant cooperative locomotion mechanism. It innovatively fuses horizontal and vertical field-of-view measurements for robust inter-agent distance estimation; introduces an intermittent motion strategy enabling reliable detection and avoidance of faulty agents without requiring high-precision fault identification; and is compatible with both Avoid-Attract and Alignment-type swarm control models—supporting both directional and distance-based coordination. Results: Extensive physics-based multi-scenario simulations demonstrate that the proposed mechanism significantly enhances swarm fault resilience, improving robustness by an order of magnitude while maintaining broad model generality.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Teamwork
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 in classifying them as faulty. Through extensive physics-based simulation experiments, we show dramatic improvements to swarm resilience when using these techniques. We show these are relevant to both distance-based Avoid-Attract models, as well as to models relying on Alignment, in a wide range of experiment settings.

Problem

• Develop robust distance estimation using visual neighbor size perception
• Introduce intermittent locomotion for reliable faulty robot detection
• Enhance swarm resilience in vision-based collective motion systems

Innovation

• Combines visual horizontal and vertical size for distance estimation
• Uses intermittent locomotion to detect and isolate faulty robots
• Enhances swarm resilience in both Avoid-Attract and Alignment models