@article{He2026, 
author = {Zhou He and Shengxin Wu and Shilong Yuan and Ning Ran},
title = {Path planning for multi-robot systems in partially unknown environments using Petri nets},
year = {2026},
journal = {Cybernetics and Intelligence},
keywords = {Discrete event systems, Petri net, multi-robot system, path planning, partially unknown environment},
url = {https://www.sciopen.com/article/10.26599/CAI.2026.9390023},
doi = {10.26599/CAI.2026.9390023},
abstract = {In this paper, we address multi-robot path planning in partially unknown environments with Boolean task specifications. In such environments, the workspace is modeled as a map that contains both known and unknown regions. Robots initially have only a priori information regarding the connectivity confidence within the unknown region, while the exact connectivity is revealed only when the robot physically traverses the region. First, a Petri net system that contains existing transitions and hypothetical transitions is designed to represent the partially unknown environments. Then, an improved sequential single-item auction algorithm that provides cost-efficient task allocation based on the expected task cost is proposed using the Petri net system. Additionally, a precomputed motion strategy graph that enables robots to autonomously select paths online using real-time sensor feedback is developed. Finally, a hardware case study involving multi-robot collaboration demonstrates the effectiveness and adaptability of the proposed method.}
}