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Collective Decision-Making on Task Allocation Feasibility

Samratul Fuady, Danesh Tarapore, Shoaib Ehsan, Mohammad D. Soorati

TL;DR

This work addresses how a swarm can determine the feasibility of assigning tasks in a decentralized manner. It introduces distributed feasibility and adopts Direct Modulation of Majority-based Decisions (DMMD) to let robots infer feasibility from local observations, using a two-state exploration–dissemination process and a quality metric $\rho_1(t)$. Simulation results in a collision-free, homogeneous setting show that the swarm converges to the correct feasibility decision when the robot-to-task ratio is far from 1, with faster consensus for larger imbalances and symmetry between feasible and infeasible outcomes. The findings highlight the practicality of decentralized feasibility evaluation for scalable swarm deployment, with future work extending to collision avoidance, heterogeneity, and more complex task structures.

Abstract

Robot swarms offer the potential to bring several advantages to the real-world applications but deploying them presents challenges in ensuring feasibility across diverse environments. Assessing the feasibility of new tasks for swarms is crucial to ensure the effective utilisation of resources, as well as to provide awareness of the suitability of a swarm solution for a particular task. In this paper, we introduce the concept of distributed feasibility, where the swarm collectively assesses the feasibility of task allocation based on local observations and interactions. We apply Direct Modulation of Majority-based Decisions as our collective decision-making strategy and show that, in a homogeneous setting, the swarm is able to collectively decide whether a given setup has a high or low feasibility as long as the robot-to-task ratio is not near one.

Collective Decision-Making on Task Allocation Feasibility

TL;DR

This work addresses how a swarm can determine the feasibility of assigning tasks in a decentralized manner. It introduces distributed feasibility and adopts Direct Modulation of Majority-based Decisions (DMMD) to let robots infer feasibility from local observations, using a two-state exploration–dissemination process and a quality metric . Simulation results in a collision-free, homogeneous setting show that the swarm converges to the correct feasibility decision when the robot-to-task ratio is far from 1, with faster consensus for larger imbalances and symmetry between feasible and infeasible outcomes. The findings highlight the practicality of decentralized feasibility evaluation for scalable swarm deployment, with future work extending to collision avoidance, heterogeneity, and more complex task structures.

Abstract

Robot swarms offer the potential to bring several advantages to the real-world applications but deploying them presents challenges in ensuring feasibility across diverse environments. Assessing the feasibility of new tasks for swarms is crucial to ensure the effective utilisation of resources, as well as to provide awareness of the suitability of a swarm solution for a particular task. In this paper, we introduce the concept of distributed feasibility, where the swarm collectively assesses the feasibility of task allocation based on local observations and interactions. We apply Direct Modulation of Majority-based Decisions as our collective decision-making strategy and show that, in a homogeneous setting, the swarm is able to collectively decide whether a given setup has a high or low feasibility as long as the robot-to-task ratio is not near one.
Paper Structure (4 sections, 1 equation, 3 figures)

This paper contains 4 sections, 1 equation, 3 figures.

Table of Contents

  1. INTRODUCTION
  2. COLLECTIVE DECISION-MAKING ON FEASIBILITY
  3. RESULTS
  4. CONCLUSION

Figures (3)

  • Figure 1: Experimental Setup for Distributed Feasibility. Numbers denote robots with the circle shadows indicating the range of observation and orange triangles representing tasks.
  • Figure 2: Percentage of robots holding opinion 1 (feasible) for different numbers of tasks in a collision-free non-unique case. The lines represent the median, and the shadows indicate the upper and lower quartiles for the corresponding colors.
  • Figure 3: Percentage of final collective decision on feasibility with varying robot-to-task ratios, each from 100 runs.