# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
###### Abstract
The existing intent recognition methods face several challenges when applied to underwater multi-AUV collaborative search missions, which are outlined as follows:  
* In contrast to other domains where intention recognition often relies on inherent or well-established mappings, such as the intuitive association between human actions and intentions, no such predefined or standardized relationship exists in the context of AUV collaborative search missions. * Unlike autonomous driving and collaborative surface or aerial robots, which can rely on high-precision sensors to obtain large volumes of high-resolution environmental data in real time, multi-AUV systems are constrained by low bandwidth, high latency, and high packet loss rates of underwater acoustic communication. These limitations restrict intent recognition to only a limited amount of data exchange. Additionally, accumulated errors in underwater navigation lead to inaccurate positioning information. These factors result in relatively low accuracy and completeness of the data used for intent recognition. To address the challenges mentioned above, we propose an intent recognition method for AUVs, termed the consensus-based intent recognition (CBIR) approach. Specifically, this method is grounded in the Belief-Desire-Intention (BDI) framework [16]. The BDI framework is a cognitive architecture widely used in autonomous systems and multi-agent environments to model rational decision-making processes. The BDI framework consists of three core components: beliefs, desires, and intentions. Belief's represent the agent's understanding of the environment, including information received from sensors, prior knowledge, and inferred data. Belief's form the foundation of the agent's perception of the current state of the world. Desires reflect the goals or objectives the agent aims to achieve. Desires are not bound by practicality; instead, they represent the ideal outcomes or end states the agent wishes to reach. Intentions represent the specific plans or actions the agent commits to executing in order to achieve its desires, given its beliefs. Intentions bridge the gap between abstract goals and practical actions, guiding the agent's behavior in a structured manner. In this study, beliefs represent the AUV's perception of the state information of both the swarm and the target. Desires refer to the goal of successfully locating the target, while intentions indicate the actions taken by the AUV, based on its understanding of global state information, to complete the collaborative search mission. The BDI framework is used to unify the decision-making process of the entire AUV swarm. Intent recognition enhances an AUV's understanding of the global situation by identifying the intentions of other AUVs, thereby optimizing its decision-making process. As previously analyzed, intent recognition in the AUV domain lacks a clear mapping between behavioral states and intentions. Therefore, the first challenge is to determine which specific state information influences AUV intentions and how this information impacts decision making. However, in collaborative search missions, it is impossible for the AUV swarm to complete the target search with a single decision. Throughout the mission, maintaining communication and improving detection accuracy must also be considered, requiring multiple dynamic adjustments to accomplish the mission. This complexity makes the mapping between behavioral states and intentions highly intricate. To address this, we introduce the concept of \"landmarks\" [17]. In this context, a \"landmark\" refers to a representative state that signifies potential short-term achievements during the intermediate stages of completing the mission. Specifically, to execute the collaborative search mission, the AUV needs to dynamically adjust its decisions at fixed intervals based on the current global situation, producing an immediate intention that serves as the \"landmark\".