Shaukat Mansoor, Chitre Mandar
Acoustics Research Laboratory, Tropical Marine Sciences Institute, Singapore.
Adapt Behav. 2016 Dec;24(6):446-463. doi: 10.1177/1059712316664120. Epub 2016 Sep 5.
In this paper, the role of adaptive group cohesion in a cooperative multi-agent source localization problem is investigated. A distributed source localization algorithm is presented for a homogeneous team of simple agents. An agent uses a single sensor to sense the gradient and two sensors to sense its neighbors. The algorithm is a set of individualistic and social behaviors where the individualistic behavior is as simple as an agent keeping its previous heading and is not self-sufficient in localizing the source. Source localization is achieved as an emergent property through agent's adaptive interactions with the neighbors and the environment. Given a single agent is incapable of localizing the source, maintaining team connectivity at all times is crucial. Two simple temporal sampling behaviors, intensity-based-adaptation and connectivity-based-adaptation, ensure an efficient localization strategy with minimal agent breakaways. The agent behaviors are simultaneously optimized using a two phase evolutionary optimization process. The optimized behaviors are estimated with analytical models and the resulting collective behavior is validated against the agent's sensor and actuator noise, strong multi-path interference due to environment variability, initialization distance sensitivity and loss of source signal.
本文研究了自适应群体凝聚力在多智能体协作源定位问题中的作用。针对由简单智能体组成的同质团队，提出了一种分布式源定位算法。一个智能体使用单个传感器来感知梯度，并使用两个传感器来感知其邻居。该算法是一组个体行为和社会行为，其中个体行为简单到智能体保持其先前的方向，并且在源定位方面不具备自足性。源定位是通过智能体与邻居和环境的自适应交互作为一种涌现特性来实现的。鉴于单个智能体无法定位源，始终保持团队连通性至关重要。两种简单的时间采样行为，即基于强度的自适应和基于连通性的自适应，确保了一种高效的定位策略，同时使智能体脱离的情况最少。通过两阶段进化优化过程对智能体行为进行同步优化。使用分析模型对优化后的行为进行估计，并针对智能体的传感器和执行器噪声、由于环境变化导致的强多径干扰、初始化距离敏感性和源信号丢失，对由此产生的集体行为进行验证。
