logicMOVE: Logic Reasoning in Motion Planning for Multiple Robotic Agents
2022 - 2024
Motion planning for multiple robotic agents (MR-MoP) is a task to find non-colliding sequences of simple movements for individual robotic agents so each agent achieves its individual goal. An important character-istic of the task is the large number of relatively simple robotic agents that can physically interact with each other in various ways. The task is based on the well-known multi-agent path finding (MAPF), but places more emphasis on the real properties of the environment in which the robotic agents operate, namely the continuity of space and time is assumed. Considering the continuity of the environment directly in abstract models can lead to more precise and mode efficient plans. The project assumes algorithmic contributions to motion planning for a multi-agent system on all important layers of common planning abstractions, i.e. from the level of (discrete) classical planning, through (continuous) motion planning, to the execution of plans with physical robots. The new algorithms will be based on the principles of logical reasoning, in particular lazy compilation approaches.
Intelligent Algorithms for Generalized Variants of Multi-Agent Path Finding
2019 - 2021
Multi-agent path finding (MAPF) is a task of finding non-colliding paths for multiple distinguishable agents in a graph. The MAPF problem represents an important theoretical challenge but also has many practical applications. Solving techniques for the standard MAPF experienced a significant progress recently for both the optimal and the sub-optimal case. This project reflects the growing interest of research community in generalizations of MAPF. Our research aims on study of intelligent solving algorithms in several diverse conceptual directions of MAPF generalizations that are unique to this project. Generalizations in logic formulations of MAPF with focus on expressing MAPF in the SAT modulo theory framework (SMT) and complex local and global constraints are studied. In adversarial variants of MAPF (AMAPF), where multiple teams of agents compete in reaching their goals, the project aims on combination of game-theoretic approach with machine learning. Worthwhile generalizations concern polynomial-time algorithms for MAPF where extensions from undirected to directed graphs are studied.