## 2 State of the Art  
### Fuzzy Agent-Based Simulation  
To effectively enhance the autonomy of AIVs, it is important to explore a range of advanced decision-making techniques that help address the problem of energy management. These problems have been tackled using different techniques in the literature [8]. For example, ref. [9] provides a function for the energy minimization problem. They use metaheuristic algorithms such as genetic algorithms and particle swarm optimization methods to create a more precise robot motion trajectory in their case, resulting in an energy-efficient robot configuration.  
Our current research focuses on the use of fuzzy agents to manage the levels of imprecision and uncertainty involved in modeling the behavior of simulated vehicles [10]. Fuzzy set theory is well suited to the processing of uncertain or imprecise information that must lead to decision-making by autonomous agents [11]. The concept of the fuzzy agent can, therefore, be proposed as a partial implementation of this theory.  
Most of the control tasks performed via autonomous mobile robots (perception, localization, mapping, path and task planning, navigation and motion control, obstacle avoidance, communication, and energy control [12]) have been the subject of performance improvement studies using fuzzy logic:  
1. The navigation of mobile robots from conceptual, theoretical, or application points of view [13], the navigation of several mobile robots [14], the navigation and control of a mobile robot in an unknown environment in real time [15], and a comparison of the navigation performance of mobile robots obtained using fuzzy logic or neural networks [16];
2. Obstacle avoidance from conceptual and systemic points of view in an unknown dynamic environment [17];
3. Path planning strategies focusing on obstacle avoidance [18] or global navigation [19];
4. Motion planning [20];
5. The localization of mobile robots [21];
6. The intelligent management of energy consumption [22]. An agent-based system is fuzzy if its agents perform fuzzy behaviors or if the knowledge they use is fuzzy. This means that agents can have the following:  
* Fuzzy knowledge (fuzzy decision rules, fuzzy linguistic variables, and fuzzy linguistic values);
* Fuzzy behaviors (the behaviors adopted by the agents as a result of fuzzy inferences);
* Fuzzy interactions, organizations, or roles [23]. Fuzzy agents can follow the evolution of fuzzy information that comes from their environment and from the agents [24]. By interpreting the fuzzy information they receive or perceive, fuzzy agents interact within a multi-agent system; they can also interact in a fuzzy manner. For example, a fuzzy agent can discriminate a fuzzy interaction value to evaluate its degree of affinity (or interest) with another fuzzy agent [25]. The different contributions of this article related to fuzzy logic are as follows:  
* Propose a system designed from the literature, in particular [26], which aims to manage decisions regarding energy levels and tasks for a robot or autonomous agent.
* Conduct a comparative analysis between threshold-based approach and the fuzzy logic model adapted for our case-study scenario proposed in Section 6.
* Refine the AIV model for energy management to take into account more realistic constraints and the possibility of AIVs communicating with each other and with infrastructure elements such as charging stations.