Extended Increasing Cost Tree Search for Non-Unit Cost Domains

Multi-agent pathfinding (MAPF) has applications in navigation, robotics, games and planning. Most work on search-based optimal algorithms for MAPF has focused on simple domains with unit cost actions and unit time steps. Although these constraints keep many aspects of the algorithms simple, they also severely limit the domains that can be used. In this paper we introduce a new definition of the MAPF problem for non-unit cost and non-unit time step domains along with new multiagent state successor generation schemes for these domains. Finally, we define an extended version of the increasing cost tree search algorithm (ICTS) for non-unit costs, with two new sub-optimal variants of ICTS: epsilon-ICTS and w-ICTS. Our experiments show that higher quality sub-optimal solutions are achievable in domains with finely discretized movement models in no more time than lower-quality, optimal solutions in domains with coarsely discretized movement models.

[1]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[2]  Robert K. Brayton,et al.  Algorithms for discrete function manipulation , 1990, 1990 IEEE International Conference on Computer-Aided Design. Digest of Technical Papers.

[3]  Carme Torras,et al.  3D collision detection: a survey , 2001, Comput. Graph..

[4]  Christer Ericson,et al.  Real-Time Collision Detection , 2004 .

[5]  David Silver,et al.  Cooperative Pathfinding , 2005, AIIDE.

[6]  Nathan R. Sturtevant,et al.  Improving Collaborative Pathfinding Using Map Abstraction , 2006, AIIDE.

[7]  Kamran Iqbal,et al.  Collision detection: A survey , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[8]  Trevor Scott Standley Finding Optimal Solutions to Cooperative Pathfinding Problems , 2010, AAAI.

[9]  Adi Botea,et al.  MAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees , 2011, J. Artif. Intell. Res..

[10]  Roni Stern,et al.  The Increasing Cost Tree Search for Optimal Multi-Agent Pathfinding , 2011, IJCAI.

[11]  Howie Choset,et al.  M*: A complete multirobot path planning algorithm with performance bounds , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Nathan R. Sturtevant,et al.  Conflict-based search for optimal multi-agent pathfinding , 2012, Artif. Intell..

[13]  Kostas E. Bekris,et al.  Multi-Agent Pathfinding with Simultaneous Execution of Single-Agent Primitives , 2021, SOCS.

[14]  Steven M. LaValle,et al.  Structure and Intractability of Optimal Multi-Robot Path Planning on Graphs , 2013, AAAI.

[15]  Nathan R. Sturtevant,et al.  Enhanced Partial Expansion A , 2014, J. Artif. Intell. Res..

[16]  Rajdeep Niyogi,et al.  DMAPP: A Distributed Multi-agent Path Planning Algorithm , 2015, Australasian Conference on Artificial Intelligence.

[17]  David Tolpin,et al.  ICBS: The Improved Conflict-Based Search Algorithm for Multi-Agent Pathfinding , 2015, SOCS.

[18]  M. Narasimha Murty,et al.  Extended Conflict-Based Search for the Convoy Movement Problem , 2015, IEEE Intelligent Systems.

[19]  Roni Stern,et al.  Efficient SAT Approach to Multi-Agent Path Finding Under the Sum of Costs Objective , 2016, ECAI.

[20]  Nathan R. Sturtevant,et al.  Using Hierarchical Constraints to Avoid Conflicts in Multi-Agent Pathfinding , 2017, ICAPS.

[21]  Nathan R. Sturtevant,et al.  Search-Based Optimal Solvers for the Multi-Agent Pathfinding Problem: Summary and Challenges , 2021, SOCS.

[22]  Jorge A. Baier,et al.  Grid Pathfinding on the 2k Neighborhoods , 2017, AAAI.

[23]  Rajdeep Niyogi,et al.  DiMPP: a complete distributed algorithm for multi-agent path planning , 2017, J. Exp. Theor. Artif. Intell..

[24]  金錫俊 1990 , 1990, Literatur in der SBZ/DDR.

[25]  Tsuyoshi Murata,et al.  {m , 1934, ACML.