One kind of robot intended for cooperative work is the multi-robot system (MRS). Robots can perform activities that would be hard for a single robot to complete on its own thanks to collective behavior. Compared to single-robot systems, multi-robot systems have the following benefits: better performance, robustness, simplicity of design, and enhanced precision for complicated tasks. Designing and creating reliable and flexible multi-robot systems to handle challenging job allocation problems is of interest to researchers in both academia and industry. Group creation, object identification and tracking, communication relaying, and self-organization are a few examples of collaborative behaviors. Assigning tasks to robots while maintaining system performance under limits is one of the trickiest parts of multi-robot systems. In this paper, a greedy algorithm is used to assign robots to nearby tasks, followed by an A* algorithm to find the shortest path for robots to perform multiple tasks, taking into account obstacle avoidance and collision avoidance. The proposed system is simulated using MATLAB by designing several scenarios that mimic the behavior of the robot during the path planning model.