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Heterogeneity in Multi-Robot Environmental Monitoring for Resolving Time-Conflicting Tasks

Connor York, Zachary R Madin, Paul O'Dowd, Edmund R Hunt

TL;DR

This paper investigates how to balance continuous environmental patrolling with urgent time-critical source localization in multi-robot systems. It compares behavioral heterogeneity (patrollers vs searchers) and sensing heterogeneity (subset sensors) using a grid-world simulator with two maps and three search strategies (PSO, ECOLI, HC-PSO). Pareto-front analysis shows behavioral heterogeneity often yields the best balance between patrol idleness and time-to-find, while sensing restrictions can still maintain balance with cost savings; heterogeneity also enhances information transfer under restricted communication. The findings offer design guidance for pre-deployment role and sensor allocation to manage time-conflicting tasks in real-world MRS deployments, with future work aimed at robotic trials and adaptive strategies.

Abstract

Multi-robot systems performing continuous tasks face a performance trade-off when interrupted by urgent, time-critical sub-tasks. We investigate this trade-off in a scenario where a team must balance area patrolling with locating an anomalous radio signal. To address this trade-off, we evaluate both behavioral heterogeneity through agent role specialization ("patrollers" and "searchers") and sensing heterogeneity (i.e., only the searchers can sense the radio signal). Through simulation, we identify the Pareto-optimal trade-offs under varying team compositions, with behaviorally heterogeneous teams demonstrating the most balanced trade-offs in the majority of cases. When sensing capability is restricted, heterogeneous teams with half of the sensing-capable agents perform comparably to homogeneous teams, providing cost-saving rationale for restricting sensor payload deployment. Our findings demonstrate that pre-deployment role and sensing specialization are powerful design considerations for multi-robot systems facing time-conflicting tasks, where varying the degree of behavioral heterogeneity can tune system performance toward either task.

Heterogeneity in Multi-Robot Environmental Monitoring for Resolving Time-Conflicting Tasks

TL;DR

This paper investigates how to balance continuous environmental patrolling with urgent time-critical source localization in multi-robot systems. It compares behavioral heterogeneity (patrollers vs searchers) and sensing heterogeneity (subset sensors) using a grid-world simulator with two maps and three search strategies (PSO, ECOLI, HC-PSO). Pareto-front analysis shows behavioral heterogeneity often yields the best balance between patrol idleness and time-to-find, while sensing restrictions can still maintain balance with cost savings; heterogeneity also enhances information transfer under restricted communication. The findings offer design guidance for pre-deployment role and sensor allocation to manage time-conflicting tasks in real-world MRS deployments, with future work aimed at robotic trials and adaptive strategies.

Abstract

Multi-robot systems performing continuous tasks face a performance trade-off when interrupted by urgent, time-critical sub-tasks. We investigate this trade-off in a scenario where a team must balance area patrolling with locating an anomalous radio signal. To address this trade-off, we evaluate both behavioral heterogeneity through agent role specialization ("patrollers" and "searchers") and sensing heterogeneity (i.e., only the searchers can sense the radio signal). Through simulation, we identify the Pareto-optimal trade-offs under varying team compositions, with behaviorally heterogeneous teams demonstrating the most balanced trade-offs in the majority of cases. When sensing capability is restricted, heterogeneous teams with half of the sensing-capable agents perform comparably to homogeneous teams, providing cost-saving rationale for restricting sensor payload deployment. Our findings demonstrate that pre-deployment role and sensing specialization are powerful design considerations for multi-robot systems facing time-conflicting tasks, where varying the degree of behavioral heterogeneity can tune system performance toward either task.

Paper Structure

This paper contains 12 sections, 3 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Simulator
  4. Signal Propagation
  5. Multi-Robot Patrol
  6. Agent Search
  7. PSO
  8. E. Coli Chemotaxis (ECOLI)
  9. Hybrid-Chemotactic PSO (HC-PSO)
  10. Experiment Configurations
  11. Results & Discussion
  12. Conclusion

Figures (3)

  • Figure 1: The two simulation maps: Cumberland (left) and Office (right), each showing the propagation of one of their three tested signal source locations in red gradient.
  • Figure 2: Pareto Frontier across all algorithms/distribution medians, "knee point" bounded in red, and frontier points are linked by dashed line. Axis is cropped to exclude "All Patroller" groups clustered near (1.0, 0.0).
  • Figure 3: PSO search success rate for each agent distribution as communication range decreases. Data from 6 agents on "Cumberland" map and 12 agents on "Office" map, and PM = True.