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SwarmCoDe: A Scalable Co-Design Framework for Heterogeneous Robot Swarms via Dynamic Speciation

Andrew Wilhelm, Josie Hughes

Abstract

Robot swarms offer inherent robustness and the capacity to execute complex, collaborative tasks surpassing the capabilities of single-agent systems. Co-designing these systems is critical, as marginal improvements in individual performance or unit cost compound significantly at scale. However, under traditional frameworks, this scale renders co-design intractable due to exponentially large, non-intuitive design spaces. To address this, we propose SwarmCoDe, a novel Collaborative Co-Evolutionary Algorithm (CCEA) that utilizes dynamic speciation to automatically scale swarm heterogeneity to match task complexity. Inspired by biological signaling mechanisms for inter-species cooperation, the algorithm uses evolved genetic tags and a selectivity gene to facilitate the emergent identification of symbiotically beneficial partners without predefined species boundaries. Additionally, an evolved dominance gene dictates the relative swarm composition, decoupling the physical swarm size from the evolutionary population. We apply SwarmCoDe to simultaneously optimize task planning and hardware morphology under fabrication budgets, successfully evolving specialized swarms of up to 200 agents -- four times the size of the evolutionary population. This framework provides a scalable, computationally viable pathway for the holistic co-design of large-scale, heterogeneous robot swarms.

SwarmCoDe: A Scalable Co-Design Framework for Heterogeneous Robot Swarms via Dynamic Speciation

Abstract

Robot swarms offer inherent robustness and the capacity to execute complex, collaborative tasks surpassing the capabilities of single-agent systems. Co-designing these systems is critical, as marginal improvements in individual performance or unit cost compound significantly at scale. However, under traditional frameworks, this scale renders co-design intractable due to exponentially large, non-intuitive design spaces. To address this, we propose SwarmCoDe, a novel Collaborative Co-Evolutionary Algorithm (CCEA) that utilizes dynamic speciation to automatically scale swarm heterogeneity to match task complexity. Inspired by biological signaling mechanisms for inter-species cooperation, the algorithm uses evolved genetic tags and a selectivity gene to facilitate the emergent identification of symbiotically beneficial partners without predefined species boundaries. Additionally, an evolved dominance gene dictates the relative swarm composition, decoupling the physical swarm size from the evolutionary population. We apply SwarmCoDe to simultaneously optimize task planning and hardware morphology under fabrication budgets, successfully evolving specialized swarms of up to 200 agents -- four times the size of the evolutionary population. This framework provides a scalable, computationally viable pathway for the holistic co-design of large-scale, heterogeneous robot swarms.

Paper Structure

This paper contains 23 sections, 3 equations, 9 figures, 5 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORKS
  3. Robot Co-Design
  4. Genetic Algorithms for Swarm Systems
  5. Cooperative Co-Evolution and Dynamic Speciation
  6. METHODS
  7. The Algorithm
  8. Genome Structure
  9. Dynamic Speciation
  10. Evaluation and Fitness
  11. Evolutionary Operators
  12. Co-Design of Robot Swarms
  13. Planning
  14. Hardware Design
  15. Simulation Environment and Task Formulation
  16. ...and 8 more sections

Figures (9)

  • Figure 1: Overview of the SwarmCoDe algorithm that dynamically determines species for the co-design of heterogeneous robot swarms. Genetic tags and a selectivity gene are used to determine partners for evaluations to complete the collaborative task. A relative dominance gene determines the swarm composition, enabling our technique to scale to swarm sizes of 200 robots, four times the number of individuals in the evolutionary population.
  • Figure 2: Evaluation pipeline for calculating marginal contribution. Swarms are stochastically assembled using the evolved dominance gene to determine swarm composition ratios. By comparing the performance of a swarm containing the focal individual against a baseline swarm where that individual is replaced by partner elites in an identical environment, the algorithm determines the true fitness of the focal individual.
  • Figure 3: The simulation environment with 20 agents and 16 individual packages. Robots are tasked with retrieving packages and returning them to the base. Robots with pincher end effectors (green and purple) can lift square packages while robots with suction end effectors (red and blue) can lift circle packages. Darker packages are heavier, and located closer to the base; lighter packages are farther away.
  • Figure 4: Emerging species and their morphological niches for increasingly complex scenarios. The SwarmCoDe algorithm adapts to increasingly complex tasks that require one, two, and four unique species to efficiently complete.
  • Figure 5: Evolutionary dynamics and fitness for the "Two Package Types with Distance-Based Weights" scenario. (Left) The species composition of the evolutionary population over time, where different colors represent distinct species. Early on, many species compete to remain in the population, with many going extinct within the first 20 generations. (Center) The species composition of the best team. As the species adapt and evolve, the best team composition changes, with some species remaining in the best team for several generations while others are never included. (Right) The fitness of the best team per generation. The fitness evolves towards more optimal values over time, but fluctuates as different species emerge and go extinct in the population.
  • ...and 4 more figures