用模拟退火让多机器人均匀采样，自动优化信息获取效率。
Simulated Annealing for Multi-Robot Ergodic Information Acquisition Using Graph-Based Discretization
- 通过模拟退火逐步调整采样分布，从均匀到最优
- 熵值提升显著，瞬态与稳态性能均优于基准方法
- 适合需要均衡覆盖的多机器人环境感知任务
多机器人协同主动信息采集的目标之一是使各区域相对不确定性保持一致，以确保观测质量均一（如目标检测一致性）。为此，可采用遍历覆盖机制，根据观测质量（即采样噪声水平）分配样本数量。然而，机器人无法预先获知噪声水平；尽管可通过样本估计，但初期估计不可靠，易导致波动。本文提出使用模拟退火生成目标采样分布：从均匀分布出发，通过调节玻尔兹曼分布的冷度参数，随估计采样熵变化逐步收敛至最优分布。仿真结果表明，该方法在瞬态和稳态熵表现上均显著优于均匀采样和直接遍历搜索。最后，通过TurtleBot集群系统演示验证了算法的物理可行性。
原文摘要 · Abstract (English)
One of the goals of active information acquisition using multi-robot teams is to keep the relative uncertainty in each region at the same level to maintain identical acquisition quality (e.g., consistent target detection) in all the regions. To achieve this goal, ergodic coverage can be used to assign the number of samples according to the quality of observation, i.e., sampling noise levels. However, the noise levels are unknown to the robots. Although this noise can be estimated from samples, the estimates are unreliable at first and can generate fluctuating values. The main contribution of this paper is to use simulated annealing to generate the target sampling distribution, starting from uniform and gradually shifting to an estimated optimal distribution, by varying the coldness parameter of a Boltzmann distribution with the estimated sampling entropy as energy. Simulation results show a substantial improvement of both transient and asymptotic entropy compared to both uniform and direct-ergodic searches. Finally, a demonstration is performed with a TurtleBot swarm system to validate the physical applicability of the algorithm.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。