Coverage Path Planning for Robot Swarms

Given the growing importance of the role of robots in human life, the use of artificial intelligence-based algorithms to enable them to perform tasks efficiently and optimally is essential. In the meantime, helping to solve mobility for robots, as one of their fundamental issues, is crucial. As a practical example, in search and rescue operations, which usually involve complex environments, robots can perform initial searches to localize survivors and gather information about their physical conditions. After that, rescuers can begin faster and safer rescue operations. However, as external infrastructure for navigation and communication is usually not available, robotic systems must operate autonomously. Robots may try to find and optimize the path between two points (Path Planning); In a maze-like environment, they could explore to find the exit (Maze Solving); a search task might be the motivation of their movement. The environment, in terms of observability, might be observable, semi-observable, or unknown. Also, in terms of complexity, it might range from simple (i.e., containing no or regularly shaped obstacles) to complex (i.e., a maze-like environment containing various complex barrier sets). In this project, we investigate algorithms for swarm robots that operate autonomously without central control, minimal communication, and movement in a non-synchronous manner (i.e., robots do not need to stop and get synced through a center in each iteration).