## 2 Literature Review  
This paper addresses the multi-agent collision avoidance problem for a system of wheeled mobile robots (WMRs). Assuming unicycle dynamics for the WMRs, the objective is to design feedback control mechanisms that enable autonomous navigation to predefined targets while avoiding collisions with obstacles and other agents [9]. To achieve this, this study leverages two key control-theoretic frameworks: control barrier functions (CBFs) and control Lyapunov functions (CLFs). CBFs ensure safety by enforcing the forward invariance of a predefined safe set of states [10], while CLFs provide asymptotic stability guarantees for trajectory tracking and goal convergence [11]. These frameworks form the foundation for developing robust and adaptive control strategies in dynamic environments. Control theory broadly aims to manipulate dynamic systems to achieve desired outputs through reference signals. Classical approaches often focus on linear time-invariant (LTI) single-input single-output (SISO) systems, which are limited in their ability to handle dynamic load variations, real-time fluctuations, and the complexity of multi-input multi-output (MIMO) systems. While modern methods prioritize robustness, adaptability, and MIMO system compatibility, they may lack the intuitive design principles of traditional approaches, complicating system analysis and implementation [12]. This trade-off between classical and modern methods highlights the need for innovative solutions that balance simplicity, adaptability, and computational efficiency. For motion planning in cluttered environments, rapidly exploring random trees (RRTs), a subclass of rapidly exploring dense trees (RDTs), offer probabilistic completeness and efficient exploration of high-dimensional configuration spaces through stochastic sampling [13]. Despite their advantages, such as computational simplicity and the ability to expand into unexplored regions, standard RRTs suffer from limitations, including slow convergence, inconsistent path quality, and inaccuracies in practical implementations [14]. These shortcomings underscore the need for more reliable and efficient motion planning techniques, particularly in dynamic and unpredictable environments. In multi-agent decision-making scenarios involving non-cooperative agents, game theory provides valuable tools for analyzing strategic interactions and optimizing collective outcomes [15]. While game theory enhances risk management and strategic reasoning, it is not without limitations. Computational complexity, the oversight of psychological factors, and idealized knowledge assumptions can hinder its practical applicability [16]. These challenges highlight the importance of integrating game-theoretic approaches with other control strategies to achieve more robust and realistic solutions. Artificial intelligence (AI) technologies further augment WMR autonomy by enabling advanced perception, language processing, data analysis, and decision-making capabilities [17]. Despite their benefits, such as reduced human error and 24/7 availability, AI-driven systems face challenges, including algorithmic bias, high deployment costs, security vulnerabilities, and limited adaptability in unstructured environments [18]. Addressing these challenges is critical for realizing the full potential of AI in autonomous systems, particularly in safety-critical applications like agriculture. Among these approaches, control barrier functions (CBFs) are particularly critical for ensuring WMR safety. CBFs provide a systematic framework to define and maintain safe sets during operation, enabling collision avoidance, resilience to disturbances, and adaptability in dynamic environments [19]. By integrating CBFs with Lyapunov-based controllers, WMRs can achieve both safety and performance goals, making them well suited for applications in smart agriculture.