Robot motion planning: a distributed representation approach
International Journal of Robotics Research
Sensor based motion planning: the hierarchical generalized Voronoi graph
Sensor based motion planning: the hierarchical generalized Voronoi graph
Sokoban: enhancing general single-agent search methods using domain knowledge
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
On Multiple Moving Objects
Planning Algorithms
Constraint-Based Multi-agent Path Planning
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
Tractable multi-agent path planning on grid maps
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Solvability of multi robot motion planning problems on trees
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Massively multi-agent pathfinding made tractable, efficient, and with completeness guarantees
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
MAPP: a scalable multi-agent path planning algorithm with tractability and completeness guarantees
Journal of Artificial Intelligence Research
The increasing cost tree search for optimal multi-agent pathfinding
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Complete algorithms for cooperative pathfinding problems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Tractable massively multi-agent pathfinding with solution quality and completeness guarantees
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Towards optimal cooperative path planning in hard setups through satisfiability solving
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
The increasing cost tree search for optimal multi-agent pathfinding
Artificial Intelligence
Push and rotate: cooperative multi-agent path planning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Interacting behavioral Petri nets analysis for distributed causal model-based diagnosis
Autonomous Agents and Multi-Agent Systems
Hi-index | 0.00 |
Multi-robot path planning is dificult due to the combinatorial explosion of the search space with every new robot added. Complete search of the combined state-space soon becomes intractable. In this paper we present a novel form of abstraction that allows us to plan much more eficiently. The key to this abstraction is the partitioning of the map into subgraphs of known structure with entry and exit restrictions which we can represent compactly. Planning then becomes a search in the much smaller space of subgraph configurations. Once an abstract plan is found, it can be quickly resolved into a correct (but possibly sub-optimal) concrete plan without the need for further search. We prove that this technique is sound and complete and demonstrate its practical effiectiveness on a real map. A contending solution, prioritised planning, is also evaluated and shown to have similar performance albeit at the cost of completeness. The two approaches are not necessarily conflicting; we demonstrate how they can be combined into a single algorithm which out-performs either approach alone.