MASS: Multi-Agent Scheduling System for Intelligent Surveillance

With the recent development of autonomous driving technology, attempts to apply autonomous mobile robots to security and surveillance have been continued. Algorithms such as localization, obstacle avoidance, and path planning are essential for indoor autonomous driving of mobile robots. Among them, in this study, we deal with the path planning algorithm of mobile robots. In particular, the goal is to generate a path that covers the entire area of the given map, focusing on patrolling and guarding. We propose an algorithm that divides the generated path into multiple paths and allocates it to multi-robots by clustering. In addition, we propose a path planning algorithm that considers weights assigned on the probability map. We evaluated the performance of robot path generation with a real-world map from a testbed at the Korea Institute of Robotics and Technology Convergence (KIRO) in Pohang, Korea. This study also presents the results of cases with and without importance weights.