# A Survey on Swarm Robotics for Area Coverage Problem
## 7 Discussion  
The variety of algorithmic approaches used in swarm robots for area coverage is highlighted in the publications that were surveyed, demonstrating how adaptable these systems are. The varied tactics are illustrated by three well-known methods: Ant Colony Optimization (ACO), Honey Bee Optimization (MBO), and Particle Swarm Optimization (PSO). The versatility of swarm robotics in tackling multiple problem areas is exemplified by this variation, which also displays the inventiveness of researchers.  
The articles that were surveyed differ significantly in their approach to exploration, task allocation, or hybrid systems that combine the two. Some research emphasizes methods for allocating and coordinating tasks, while others centre on the discovery of previously uncharted settings. A good example of the complex decision-making required for algorithm selection is the fact that the choice is frequently conditional on whether the area coverage problem has static or dynamic obstacles.  
The reliability of data sources is highly related to how well swarm robots perform in area coverage tasks. For robots to understand and move around in their environments, sensing technologies like GPS, infrared cameras, and light detection and ranging (LiDAR) are crucial. It turns out that the swarm's adaptability and coverage of a specific area are heavily impacted by the sensing technology that is chosen.  
The papers that were analysed cover a wide range of practical fields, such as healthcare, exploration, mapping, and agriculture, among others. Because every application model has its own specific needs and difficulties, it is clear that we need tailored algorithms and coordination strategies. The effectiveness and versatility of swarm robotics systems can be greatly improved by adapting them to different industries and environments.  
The development of swarm robotics for the coverage of large areas can be shown by looking at the research that was surveyed over time. More efficient algorithms, better hardware integration, and more useful applications are all the results of recent developments. The maturation and greater popularity of swarm robots over the years have made it a generally accepted solution for complicated area coverage challenges.  
There have been some successes, but there are still some obstacles in the field of swarm robots. Recurring limitations brought up by the papers under consideration include scalability, adaptability to new situations, and the creation of strong coordination mechanisms. In order to direct future research toward resolving these constraints, it is critical to acknowledge these obstacles.  
The publications that were surveyed highlight the significance of hardware in swarm robots. Improved capabilities are displayed by robots like GRITSBot, TurtleBot3, and e-puck 2 that have complex control systems, high-tech sensors, and strong networking capabilities. When the swarm's gear is well-integrated, it increases their efficiency and lets them cover more area.  
By highlighting open topics in the field, the survey points the way toward potential future research and development directions. Possible future research avenues include adaptive algorithms, swarm robotics integration with new technology, and dynamic obstacle avoidance. Swarm robotics applications can be improved, and the industry can advance if these knowledge gaps are filled.  
Final observations on algorithmic diversity, methodological considerations, sensing technology influence, application-specific challenges, evolution over time, persistent obstacles, hardware's role, and possible future research directions are presented in the detailed discussion. All of these findings add up to a full picture of where swarm robots for area coverage stand right now.