异构机器人团队通过行为熵实现高效分布式探索任务分配
Behaviorally Heterogeneous Multi-Agent Exploration Using Distributed Task Allocation
- 基于行为熵评估前沿点价值，用非合作博弈求解任务分配
- 算法在仿真中收敛快、通信开销低，异构团队探索效率更高
- 适用于多机器人系统，尤其适合感知能力不同的团队
我们研究了具有行为异构性的多机器人探索问题。每台机器人使用SLAM构建环境地图，并识别出一组最具信息量的感兴趣区域（AoIs）或前沿点。机器人利用行为熵（BE）评估前往某一前沿点的效用，并通过分布式任务分配方案决定行动目标。我们将任务分配问题转化为非合作博弈，采用分布式算法（d-PBRAG）收敛至纳什均衡（我们证明其为最优任务分配解）。针对未知效用情况，我们提供了基于近似奖励的鲁棒边界。我们在仿真中测试该算法（具有较低通信成本和快速收敛性），研究了传感半径、传感精度以及机器人团队异质性对探索完成时间与路径长度的影响。结果表明，具备异构行为的机器人团队更有利于提升探索效率。
原文摘要 · Abstract (English)
We study a problem of multi-agent exploration with behaviorally heterogeneous robots. Each robot maps its surroundings using SLAM and identifies a set of areas of interest (AoIs) or frontiers that are the most informative to explore next. The robots assess the utility of going to a frontier using Behavioral Entropy (BE) and then determine which frontier to go to via a distributed task assignment scheme. We convert the task assignment problem into a non-cooperative game and use a distributed algorithm (d-PBRAG) to converge to the Nash equilibrium (which we show is the optimal task allocation solution). For unknown utility cases, we provide robust bounds using approximate rewards. We test our algorithm (which has less communication cost and fast convergence) in simulation, where we explore the effect of sensing radii, sensing accuracy, and heterogeneity among robotic teams with respect to the time taken to complete exploration and path traveled. We observe that having a team of agents with heterogeneous behaviors is beneficial.
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