# An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation
## 7 Conclusions  
This study first attempted to survey the collision-free multi-robot aggregation problem and, consequently, developed a novel robotic edge intelligence model to address the CMAP. Our work includes four main contributions, as follows: First, four metrics of the collision-free multi-robot aggregation problem were considered, including the minimum makespan, collision avoidance, energy consumption, and total travel distance. Second, a novel heuristic algorithm is designed to search for the optimal solution, namely, the artificial plant community algorithm. Third, a newly developed benchmark test set based on the existing literature was explored. The experimental results demonstrated that the proposed APC algorithm presents improved global and local search capabilities, and is superior to some state-of-the-art methods in solving the CMAP. Regarding the efficiency of task execution, the proposed APC algorithm improved makespan \(k_{T}^{make}\) by up to 3.06%, the total energy consumption \(e_{T}^{total}\) by up to 3.15%, and the total travel distance \(D_{T}^{travel}\) by up to 2.68%, while maintaining no collisions (\(k_{T}^{coll}=0\)). At the same time, the proposed APC method is lightweight; as such, it can be easily deployed on the embedded platforms of edge robots. The performance comparison demonstrated that the APC algorithm can efficiently instruct the edge robots to aggregate and handle tasks without collision.  
However, there are still some shortcomings that need to be further resolved. First, while this work built a multi-objective function for the CMAP, it was based on a certain environment and assumptions. For engineering applications, it is required to consider more factors and uncertain environments to ensure collision avoidance. Second, our edge intelligence approach was not deployed in real-world scenarios and, in future research, how to deploy and validate edge intelligence in real-world scenarios is an important direction. This work investigated some main metrics, but other metrics were not considered--such as the collision rate, path efficiency, convergence time, and resource utilization--which should be further surveyed in the future. Third, there may be differences between running the proposed algorithm in systems characterized by edge computing and those without edge computing, potentially leading to hardware limitations, communication delays, sensor inaccuracies, and environmental uncertainties, which are not considered in this manuscript. In the future, it will be necessary to further study the developed mathematical model and working processes, as well as conducting further comparative experiments. Finally, even though the proposed APC can efficiently handle the collision-free multi-robot aggregation problem, it will be important to test and analyze the impacts of environment size, obstacle density, and computational resources on performance in the future, using more real-world benchmark data and statistical tools.  
Conceptualization, Z.C.; Methodology, Z.C.; Software, Z.L. ([PERSON]); Validation, Z.L. ([PERSON]) and Z.L. ([PERSON]); Investigation, Z.L. ([PERSON]); Data curation, Q.Y. and Z.L. ([PERSON]); Writing - original draft, Q.Y.; Writing - review & editing, Q.Y.; Project administration, Z.C. and G.G.; Funding acquisition, Z.C. and G.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (No. 71471102), Major Science and Technology Projects in Hubei Province of China (Grant No. 2020 AEA012), and Yichang University Applied Basic Research Project in China (Grant No. A17-302-a13).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.