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Grasshoppers may not spring to mind as paragons of graceful flight. But for a team of Princeton engineers, these gangly insects have inspired a new approach to robotic wings.

Bees navigate their surroundings with astonishing precision. Their brains are now inspiring the design of tiny, low-power chips that could one day guide miniature robots and sensors.

Researchers at Japan’s National Institute of Informatics and collaborators built an insect-inspired robot that can keep tracking an odor source even when one of its two odor sensors fails, by copying the adaptive behavior of silkmoths, which can still navigate with only one antenna.

Leaving breadcrumbs (well, pheronome trails) to lead the way. https://pmc.ncbi.nlm.nih.gov/articles/PMC10956014/

This article reports on a robot system inspired by how ants communicate using pheromone trails. Instead of real chemicals, the robots follow artificial “pheromone” signals so they can coordinate with one another and make group decisions in a similar way to ants.

Fractals! Ant colonies, materials like gases and liquids, and robot swarms may all follow a similar underlying pattern: lots of individuals behaving somewhat randomly, but still producing large-scale collective behaviour.

Honeybees do more than just follow the “direction and distance” in another bee’s waggle dance. They seem to combine that message with their own memory of landmarks in the landscape, so they can form an expectation of what the route should look like and fly more efficiently.

This paper looked at how honey bees pass on food-location information through the waggle dance, and found that these recruitment networks are actually quite thin rather than densely connected.

The algorithm models ant nest-selection behaviour to solve the “best-of-n” consensus problem using only local signalling. It demonstrates how response thresholds, recruitment, and biasing mechanisms enable robust distributed agreement without central control.

The accelerator targets startups explicitly using biological signalling, coordination, and adaptation principles. These programs often surface applied implementations of swarm intelligence and collective sensing before they appear in formal literature.