## 5 Conclusions  
This paper presents a collision avoidance system for smart agriculture using a Control barrier function (CBF)-based approach. The system enables wheeled mobile robots (WMRs) to navigate agricultural fields safely by monitoring speed, distance, and potential hazards, thereby reducing the risk of collisions. Collision avoidance systems (CASs) are critical in agricultural environments, where the presence of static obstacles, dynamic conditions, and multiple agents necessitates robust safety mechanisms. By automatically applying braking mechanisms when collisions become unavoidable, these systems minimize the impact of accidents, reducing injuries, damage to WMRs, and crop losses. This study focuses on enhancing the safety and collision mitigation capabilities of smart agriculture systems through the integration of a CBF-based controller into the WMR framework. The multi-agent collision avoidance problem is addressed within a centralized framework, where each agent has continuous access to the positions of all other group members. This setup considers a set of \(N\) WMRs operating on the Euclidean plane, navigating around static obstacles. Each WMR is modeled as a unicycle with a passive wheel, a common representation for mobile robots due to its simplicity and applicability to real-world scenarios. Designing a CBF that ensures the full forward invariance of the safe set is a challenging task. However, constructing a CBF that partially recovers the safe set is more feasible and practical for real-world applications. The control algorithms utilize feedback signals to determine whether it is safe to execute desired control actions. These signals are analyzed for safety using CBF quadratic programming (CBF-QP) and then sent to a data buffer for model-based rollout with input selection. This approach ensures that the system operates within safe bounds while maintaining efficiency and responsiveness. In agricultural environments, collision avoidance relies on WMRs leveraging visual signals and situational awareness to navigate through single and double conflicting conditions. Initially, the control barriers are reduced and periodically adjusted to initial conditions before gradually increasing until equilibrium is achieved. Over time, both the control barriers and the control effort decrease, but at different rates: the control barriers decrease moderately, while the control effort diminishes rapidly. This differential reduction ensures that the system maintains stability and safety while minimizing energy consumption. Control barrier functions (CBFs) are mathematical tools in control theory that enforce safety requirements during system operation, analogous to how Lyapunov functions ensure stability. By providing a systematic framework to guarantee that a system operates within predefined safe bounds, CBFs significantly enhance the safety and autonomy of robotic systems. This is particularly important in dynamic and unpredictable environments, such as agricultural fields, where obstacles and terrain conditions can change rapidly. Performance comparisons demonstrate that the CBF-QP approach outperforms both rapidly exploring random trees (RRT) and adaptive model predictive control (AMPC). For WMR position, CBF-QP improves settling time by \(11\%\), rise time by \(34\%\), and steady-state error by \(75\%\). For WMR velocity, the improvements are even more pronounced, with \(27\%\) in settling time, \(37\%\) in rise time, and \(99\%\) in steady-state error. Compared to AMPC, CBF-QP achieves a \(98\%\) improvement in steady-state error for position, \(99\%\) for velocity, and \(94\%\) for control effort gain. These results highlight the superior efficiency and precision of the CBF-QP approach, making it well suited for collision avoidance in smart agriculture. The authors recommend further exploration of a hybrid approach that integrates CBF with RRT (CBF-RRT) to enhance motion planning.