activity
20172023
most citedMulti-agent Path Finding with Continuous Time Viewed Through Satisfiability Modulo Theories (SMT)

3 citations · 7 across the 10 of their papers we have counts for

collaborators

15 papers

cs.MA2023

Multi-Agent Path Finding with Continuous Time Using SAT Modulo Linear Real Arithmetic

Tomáš Kolárik, Stefan Ratschan, Pavel Surynek

This paper introduces a new approach to solving a continuous-time version of the multi-agent path finding problem. The algorithm translates the problem into an extension of the cla…

cs.AI2021

Compilation-based Solvers for Multi-Agent Path Finding: a Survey, Discussion, and Future Opportunities

Pavel Surynek

Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community as well as in robotics, and other fields such as warehouse logistics. The task…

cs.RO2021

Sparsification for Fast Optimal Multi-Robot Path Planning in Lazy Compilation Schemes

Pavel Surynek

Path planning for multiple robots (MRPP) represents a task of finding non-colliding paths for robots through which they can navigate from their initial positions to specified goal…

cs.AI2020

Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering

Pavel Surynek

We introduce multi-goal multi agent path finding (MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigat…

cs.AI2020

At-Most-One Constraints in Efficient Representations of Mutex Networks

Pavel Surynek

The At-Most-One (AMO) constraint is a special case of cardinality constraint that requires at most one variable from a set of Boolean variables to be set to TRUE. AMO is important…

cs.AI2020

Pushing the Envelope: From Discrete to Continuous Movements in Multi-Agent Path Finding via Lazy Encodings

Pavel Surynek

Multi-agent path finding in continuous space and time with geometric agents MAPF is addressed in this paper. The task is to navigate agents that move smoothly between…