An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search
An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search
复制标题
一种分布式群体机器人搜索的扩展贝叶斯优化方法
DOI：
10.1115/1.4046587
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
P. Ghassemi;Souma Chowdhury
中科院分区：
文献类型：
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作者：
P. Ghassemi;Souma Chowdhury
群机器人搜索的目标是使用大量协作的简单移动的机器人搜索目标，应用于搜索和救援以及危险定位。在这方面，分散式群系统因其覆盖范围可扩展性，时间效率和容错性而受到吹捧。为了指导这种群体系统的行为，有两大类方法可用，即自然启发的群体算法和多机器人搜索方法。然而，同时实现有效的可扩展性和提供对所展示的行为的基本见解（与展示黑盒行为相反）的能力仍然是一个悬而未决的问题。为了解决这个问题，本文扩展了底层的搜索方法，在批量贝叶斯优化执行搜索与具体的群体代理在（模拟）物理2D竞技场。主要贡献在于（1）设计一个采集功能，不仅平衡整个群体的探索和利用，而且还允许对轨迹进行建模知识提取，以及（2）开发其分布式实现，以允许群机器人进行异步任务推理和路径规划。由此产生的集体信息路径规划方法进行测试的目标搜索的情况下，研究不同的复杂性，目标产生的空间变化（可测量）的信号。值得注意的是，上级性能，在使命完成效率方面，观察到相比，穷举搜索和随机游走基线，以及基于群优化的国家的最先进的方法。有利的可扩展性特征也被证明。
Swarm robotic search aims at searching targets using a large number of collaborating simple mobile robots, with applications to search and rescue and hazard localization. In this regard, decentralized swarm systems are touted for their coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely, nature-inspired swarm heuristics and multi-robotic search methods. However, the ability to simultaneously achieve efficient scalability and provide fundamental insights into the exhibited behavior (as opposed to exhibiting a black-box behavior) remains an open problem. To address this problem, this paper extends the underlying search approach in batch-Bayesian optimization to perform search with embodied swarm agents operating in a (simulated) physical 2D arena. Key contributions lie in (1) designing an acquisition function that not only balances exploration and exploitation across the swarm but also allows modeling knowledge extraction over trajectories and (2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target-search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Notably, superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines as well as a swarm optimization-based state-of-the-art method. Favorable scalability characteristics are also demonstrated.