A Simple Decentralized 3D Collision-Avoidance Method for Mobile Agents Inspired by Animal Attention

Inspired by the attention mechanisms observed in animals, we propose a perceptually grounded and decentralized collision-avoidance model for multiple autonomous mobile agents in three-dimensional (3D) space. As the density of such agents is expected to increase in applications including aerial robots (e.g., drones), there is a growing need for lightweight, prediction-free control laws that ensure safety, quickness, and smooth motion. In the proposed model, each agent represents neighboring agents on a spherical screen corresponding to its visual field and evaluates two simple attention-like perceptual indices: a rate-of-approach index derived from an increase in apparent diameter under nearly stationary viewing direction, and a proximity index derived from the diameter itself. The velocity of each agent is updated based on variations in the viewing direction on a spherical screen, which provide the directional correction for collision avoidance. This update is realized through a combination of goal-directed and local avoidance terms, without prediction, optimization, or communication. Systematic simulations with extensive parameter sweeps demonstrate that the proposed model achieves a good balance of quickness, smoothness, and safety across multiple interaction scenarios, highlighting its potential as a practical and scalable control principle for 3D multi-agent systems.