Aggregating swarms through morphology handling design contingencies: from the sweet spot to a rich expressivity

2026-01-12

AI Summary

This study addresses the challenge of modulating phototactic aggregation in robot swarms under the constraint that agents can only decelerate but not fully stop. By integrating physical exoskeletons with control strategies, the work proposes the concept of a “self-alignment strength sweet spot” to tune the self-alignment intensity of Kilobot swarms under external perturbations. It demonstrates that the coupling between morphological design and control critically influences collective behavior. Using a physical robot platform equipped with custom exoskeletons, complemented by high-fidelity simulations and real-world experiments, the study shows that moderate self-alignment significantly enhances task success in phototaxis and elicits diverse emergent swarm dynamics.

Technology Category

• Intelligent Robots: Manipulation
• Multiagent Systems: Teamwork
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Morphological computing, the use of the physical design of a robot to ease the realization of a given task has been proven to be a relevant concept in the context of swarm robotics. Here we demonstrate both experimentally and numerically, that the success of such a strategy may heavily rely on the type of policy adopted by the robots, as well as on the details of the physical design. To do so, we consider a swarm of robots, composed of Kilobots embedded in an exoskeleton, the design of which controls the propensity of the robots to align or anti-align with the direction of the external force they experience. We find experimentally that the contrast that was observed between the two morphologies in the success rate of a simple phototactic task, where the robots were programmed to stop when entering a light region, becomes dramatic, if the robots are not allowed to stop, and can only slow down. Building on a faithful physical model of the self-aligning dynamics of the robots, we perform numerical simulations and demonstrate on one hand that a precise tuning of the self-aligning strength around a sweet spot is required to achieve an efficient phototactic behavior, on the other hand that exploring a range of self-alignment strength allows for a rich expressivity of collective behaviors.

Problem

• morphological computing
• swarm robotics
• self-alignment
• phototaxis
• collective behavior

Innovation

• morphological computing
• swarm robotics
• self-alignment
• collective behavior
• physical design