Results 1 
5 of
5
Subdimensional expansion for multirobot path planning.
 Artificial Intelligence
, 2015
"... Abstract Planning optimal paths for large numbers of robots is computationally expensive. In this paper, we introduce a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as need ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Abstract Planning optimal paths for large numbers of robots is computationally expensive. In this paper, we introduce a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed. More specifically, subdimensional expansion initially creates a onedimensional search space embedded in the joint configuration space of the multirobot system. When the search space is found to be blocked during planning by a robotrobot collision, the dimensionality of the search space is locally increased to ensure that an alternative path can be found. As a result, robots are only coordinated when necessary, which reduces the computational cost of finding a path. We present the M* algorithm, an implementation of subdimensional expansion that adapts the A* planner to perform efficient multirobot planning. M* is proven to be complete and to find minimal cost paths. Simulation results are presented that show that M* outperforms existing optimal multirobot path planning algorithms.
MonteCarlo Fork Search for Cooperative PathFinding
"... Abstract. This paper presents MonteCarlo Fork Search (MCFS), a new algorithm that solves Cooperative PathFinding (CPF) problems with simultaneity. The background is MonteCarlo Tree Search (MCTS) and Nested MonteCarlo Search (NMCS). Regarding MCTS, the key idea of MCFS is to build a tree balanced ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract. This paper presents MonteCarlo Fork Search (MCFS), a new algorithm that solves Cooperative PathFinding (CPF) problems with simultaneity. The background is MonteCarlo Tree Search (MCTS) and Nested MonteCarlo Search (NMCS). Regarding MCTS, the key idea of MCFS is to build a tree balanced over the whole game tree. To do so, after a simulation, MCFS stores the whole sequence of actions in the tree, which enables MCFS to fork new sequences at any depth in the built tree. This idea fits CPF problems in which the branching factor is too large for MCTS or A * approaches, and in which congestion may arise at any distance from the start state. With sufficient time and memory, Nested MCFS (NMCFS) solves congestion problems in the literature finding better solutions than the stateoftheart solutions, and it solves Npuzzles without hole nearoptimally. The algorithm is anytime and complete. The scalability of the approach is shown for gridsize up to 200 × 200 and up to 400 agents. 1
Push and Rotate: a Complete Multiagent Pathfinding Algorithm
, 2014
"... Multiagent Pathfinding is a relevant problem in a wide range of domains, for example in robotics and video games research. Formally, the problem considers a graph consisting of vertices and edges, and a set of agents occupying vertices. An agent can only move to an unoccupied, neighbouring vertex, ..."
Abstract
 Add to MetaCart
(Show Context)
Multiagent Pathfinding is a relevant problem in a wide range of domains, for example in robotics and video games research. Formally, the problem considers a graph consisting of vertices and edges, and a set of agents occupying vertices. An agent can only move to an unoccupied, neighbouring vertex, and the problem of finding the minimal sequence of moves to transfer each agent from its start location to its destination is an NPhard problem. We present Push and Rotate, a new algorithm that is complete for Multiagent Pathfinding problems in which there are at least two empty vertices. Push and Rotate first divides the graph into subgraphs within which it is possible for agents to reach any position of the subgraph, and then uses the simple push, swap, and rotate operations to find a solution; a postprocessing algorithm is also presented that eliminates redundant moves. Push and Rotate can be seen as extending Luna and Bekris’s Push and Swap algorithm, which we showed to be incomplete in a previous publication. In our experiments we compare our approach with the Push and Swap, MAPP, and Bibox algorithms. The latter algorithm is restricted to a smaller class of instances as it requires biconnected graphs, but can nevertheless be considered state of the art due to its strong performance. Our experiments show that Push and Swap suffers from incompleteness, MAPP is generally not competitive with Push and Rotate, and Bibox is better than Push and Rotate on randomly generated biconnected instances, while Push and Rotate performs better on grids.