通过角色分工提升多机器人系统应对紧急任务的能力
Heterogeneity in Multi-Robot Environmental Monitoring for Resolving Time-Conflicting Tasks
- 让部分机器人专攻巡逻，部分专攻搜寻，实现任务分工
- 半数机器人带传感器时，性能接近全量部署团队
- 适合需平衡常规巡检与紧急响应的救援或监控场景
执行持续性任务的多机器人系统在遭遇紧急、时限紧迫的子任务时，常面临性能权衡。本文研究团队在兼顾区域巡逻与定位异常无线信号之间的冲突场景中，考察行为异质性（如“巡逻者”与“搜寻者”角色分化）和感知异质性（仅搜寻者可探测信号）的影响。通过仿真分析不同团队构成下的帕累托最优权衡，发现多数情况下行为异质性团队表现最为均衡。当感知能力受限时，仅一半机器人配备传感器的异质团队性能可媲美同质团队，为减少传感器负载部署提供成本优化依据。研究证明，提前规划角色与感知分工是应对时间冲突任务的高效设计策略，通过调节行为异质程度可灵活调控系统对两类任务的侧重。
原文摘要 · Abstract (English)
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.
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