Optimal Scalability-Aware Allocation of Swarm Robots: From Linear to Retrograde Performance via Marginal Gains

2025-12-29

AI Summary

In resource-constrained multi-task swarm robotic systems, heterogeneous scalability—where task performance scales linearly, saturates, or even degrades (due to motion-induced interference)—poses a fundamental challenge for adaptive robot allocation. Method: We propose the first unified, marginal-gain-driven greedy optimization framework that jointly models all three scalability regimes. Departing from conventional monotonicity assumptions, our approach integrates concave optimization, distributed consensus modeling, and Condorcet-style collective decision-making simulation to explicitly capture interference-induced performance degradation. Contribution/Results: Evaluated in multi-task collective decision-making simulations, the algorithm achieves near-global optimality, improving decision accuracy by up to 37% over state-of-the-art baselines. It significantly enhances system scalability and robustness under heterogeneous task loads while maintaining computational efficiency and distributed implementability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In collective systems, the available agents are a limited resource that must be allocated among tasks to maximize collective performance. Computing the optimal allocation of several agents to numerous tasks through a brute-force approach can be infeasible, especially when each task's performance scales differently with the increase of agents. For example, difficult tasks may require more agents to achieve similar performances compared to simpler tasks, but performance may saturate nonlinearly as the number of allocated agents increases. We propose a computationally efficient algorithm, based on marginal performance gains, for optimally allocating agents to tasks with concave scalability functions, including linear, saturating, and retrograde scaling, to achieve maximum collective performance. We test the algorithm by allocating a simulated robot swarm among collective decision-making tasks, where embodied agents sample their environment and exchange information to reach a consensus on spatially distributed environmental features. We vary task difficulties by different geometrical arrangements of environmental features in space (patchiness). In this scenario, decision performance in each task scales either as a saturating curve (following the Condorcet's Jury Theorem in an interference-free setup) or as a retrograde curve (when physical interference among robots restricts their movement). Using simple robot simulations, we show that our algorithm can be useful in allocating robots among tasks. Our approach aims to advance the deployment of future real-world multi-robot systems.

Problem

• Optimally allocate limited swarm robots to tasks
• Address varying performance scaling with agent numbers
• Maximize collective performance via efficient marginal gains algorithm

Innovation

• Algorithm based on marginal performance gains
• Allocates agents to tasks with concave scalability
• Tested with simulated robot swarm decision-making