## 1 Introduction  
By monitoring speed, distance, and potential hazards, collision avoidance systems (CASs) enable wheeled mobile robots (WMRs) to prevent collisions and reduce crash risks in agricultural environments [1]. Multi-agent systems have recently gained significant attention due to their broad applications in distributed robotics, mobile sensor networks, air traffic management, and agricultural automation. These systems consist of multiple interacting agents that collectively perform tasks more efficiently than individual complex agents. However, their implementation often requires novel control strategies, particularly in scenarios like formation control and collision avoidance [2]. Existing robot navigation methods frequently rely on navigation functions mathematical constructs derived from environmental geometry to design gradient-based control laws. Other approaches draw inspiration from natural swarm behaviors observed in bird flocks, fish schools, and bee colonies [3]. Control barrier functions (CBFs) offer additional advantages by ensuring safety across diverse operational conditions through adaptable parameter configurations [4]. Enhanced safety in agricultural fields not only minimizes accidents [5] but also improves operational efficiency and cost savings by ensuring uninterrupted WMR functionality [6]. Despite these advancements, existing research lacks a unified framework for evaluating WMR performance in real-time optimization-based control systems. Furthermore,safety conditions often defined via the forward invariance of safe sets remain insufficiently validated for agricultural WMR applications [7]. The aim of this study is to address the multi-agent collision avoidance problem within a centralized framework, where each agent has continuous access to the positions of all other group members. This approach is particularly relevant in agricultural settings, where multiple wheeled mobile robots (WMRs) often operate simultaneously, necessitating coordinated collision avoidance strategies. By leveraging control barrier functions (CBFs), this study ensures that each WMR can navigate safely while avoiding both static obstacles and other moving agents. This represents a significant improvement over traditional single-agent navigation methods, as it enhances safety and efficiency in dynamic and complex agricultural environments. The novelty of this work lies in the application of control barrier function-based quadratic programming (CBF-QP) for real-time, safety-critical collision avoidance in dynamic agricultural environments. While the use of control Lyapunov functions (CLFs) for asymptotic stability in trajectory tracking is well established, the key innovation here is the focus on safety-critical control through the integration of CBFs with QP. This approach ensures forward invariance of safe sets, which is crucial for navigating unpredictable agricultural environments where collision avoidance is essential [8]. The proposed CBF-QP framework provides a robust solution for enforcing safety constraints in real time, enabling wheeled mobile robots (WMRs) to avoid collisions while maintaining performance. Unlike traditional methods that primarily focus on stability and trajectory tracking, CBFs offer a systematic way to ensure the system operates within safe bounds, even in the presence of dynamic obstacles and disturbances. This is particularly relevant in agricultural settings, where obstacles such as crops, other robots, or static barriers are common. By combining CBFs with QP, the proposed method represents a significant advancement over traditional approaches, making it highly suitable for smart agricultural systems. The integration of these techniques ensures both safety and efficiency, addressing the unique challenges of real-time collision avoidance in dynamic environments. The remainder of this work is structured as follows: Section 2 reviews the relevant literature, while Section 3 details the WMR's mathematical model and control design.