# An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation
## 2 Related Work
### Multi-Robot Aggregation Problem  
Due to the fact that collaboration between multiple robots can greatly improve work efficiency and allows for the completion of tasks that a single robot cannot handle, multi-robot systems have begun to be widely applied, leading to the emergence of the multi-robot aggregation problem. An aggregation of multiple robots can form swarm intelligence that a single robot does not possess, while the failure of one or several of the robots will not affect the normal operation of the entire multi-robot system [2].  
To solve the multi-robot aggregation problem, one must determine how to assign multiple robots to multiple tasks in a way that optimizes the scheduling performance of the multi-robot system. Many researchers have proposed a variety of methods to address the multi-robot aggregation problem, which can be categorized into static coordination strategies and dynamic methods.  
First, static coordination strategies involve pre-arranging multiple robots to coordinate and execute each task simultaneously. A recruitment coordination strategy is a commonly used method to address the multi-robot aggregation problem through robot recruitment. If the business increment of a task is greater than the ability of the assigned robot and the robot cannot complete the task alone, more robots will be aggregated to complete the task collaboratively. In [20], a mobile federated learning approach with unreliable participants and selective aggregation was proposed. Static coordination strategies often require an integrated coordination center to provide a theoretically optimal task assignment for the small-scale static multi-robot aggregation problem, where many factors are ignored. Integrated coordination strategies allow for the pre-generation of a task assignment within a short time [21]. As these strategies do not consider the dynamic task environment during the task implementation process, they may become ineffective in many dynamic scenarios, such as fire extinguishing and earthquake rescue. Therefore, the quality of the pre-planned solutions under static coordination strategies may not meet the actual dynamic needs.  
Therefore, dynamic methods employ real-time searches to obtain a good balance between makespan and the workloads of robots. The multi-man-robot problem is often modeled as a mixed-integer programming (MIP) model [22], which cannot be solved in advance. Dynamic algorithms do not require manual intervention during the task scheduling process and can obtain near-optimal solutions in a relatively limited time; such algorithms include the GA [3], FL [5; 6], RL [9], and flocking-based self-organized aggregation [15]. Dynamic methods can be applied in many uncertain scenarios, where each distributed node can dynamically produce a feasible task assignment to balance the makespan of all tasks. These dynamic algorithms often employ a heuristic search mechanism to obtain good performance when solving the multi-robot aggregation problem [23]. However, the search behaviors of heuristic algorithms often lead to their premature limitation to localoptimal solutions. As such, it is difficult to apply them to adjust the solution according to the dynamic task environment in real time.