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.

中文翻译：

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使用被动感知的多智能体源定位中的自适应行为

在本文中，研究了自适应群体凝聚力在协作多智能体源定位问题中的作用。提出了一种分布式源定位算法，适用于简单代理的同构团队。代理使用单个传感器来感知梯度，使用两个传感器来感知其邻居。该算法是一组个人主义和社会行为，其中个人主义行为就像保持其先前方向的代理一样简单，并且不能自给自足地定位来源。通过代理与邻居和环境的自适应交互，源定位是作为一种紧急属性实现的。鉴于单个代理无法定位源，因此始终保持团队连接至关重要。两种简单的时间采样行为，基于强度的适应和基于连接的适应，确保有效的定位策略和最小的代理分离。使用两阶段进化优化过程同时优化代理行为。使用分析模型估计优化的行为，并针对代理的传感器和执行器噪声、由于环境可变性造成的强多径干扰、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。