Adapted Conflict Detection for Conflict Based Search

Mobile robots are increasingly deployed in various applications, including autonomous vehicles and logistics. Conflict-Based Search (CBS) is a promising approach for Multi-Agent Path Finding (MAPF), but has limitations when applied to real-world scenarios. This paper explores the challenges of adapting CBS to real-world mobile robotics, focusing on additional conflicts caused by imperfect navigation. We propose an Adaptive Conflict Detection (ACD) approach that proactively identifies conflicts within a rolling time window, making CBS more suitable for real-world applications.
Both virtual and real robots are used to evaluate the importance of an adaptation to CBS if adapted to real scenarios.
Experimental results show that ACD outperforms traditional CBS when penalties for conflict resolution are applied, demonstrating its potential for improved performance and reliability in practical multi-agent path planning applications.