Improving Continuous-time Conflict Based Search


  • Anton Andreychuk Peoples' Friendship University of Russia (RUDN University) Federal Research Center for Computer Science and Control of Russian Academy of Sciences
  • Konstantin Yakovlev Federal Research Center for Computer Science and Control of Russian Academy of Sciences HSE University
  • Eli Boyarski Ben-Gurion University of the Negev
  • Roni Stern Ben-Gurion University Palo Alto Research Center



Multiagent Planning, Heuristic Search, Motion and Path Planning


Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. However, the scalability of CCBS is limited because it does not include any known improvements of CBS. In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. These adaptions are not trivial, and require careful handling of different types of constraints, applying a generalized version of the Safe interval path planning (SIPP) algorithm, and extending the notion of cardinal conflicts. We evaluate the effect of the suggested enhancements by running experiments both on general graphs and 2^k-neighborhood grids. CCBS with these improvements significantly outperforms vanilla CCBS, solving problems with almost twice as many agents in some cases and pushing the limits of multi-agent path finding in continuous-time domains.




How to Cite

Andreychuk, A., Yakovlev, K., Boyarski, E., & Stern, R. (2021). Improving Continuous-time Conflict Based Search. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 11220-11227.



AAAI Technical Track on Multiagent Systems