Time enhanced A∗: Towards the development of a new approach for Multi-Robot Coordination

In this paper the authors focus on presenting a new path planning approach for a multi-robot transportation system in an industrial case scenario. The proposed method is based on the A* heuristic search in a cell decomposition scenario, for which a time component was added - Time Enhanced A* or simply TEAA*. To access the flexibility and efficiency of the proposed algorithm, a set of experiments were performed in a simulated industrial environment. During trials execution the proposed algorithm has shown high capability on preventing/dealing with the occurrence of deadlocks in the transportation system.

[1]  Iris F. A. Vis,et al.  Survey of research in the design and control of automated guided vehicle systems , 2006, Eur. J. Oper. Res..

[2]  Liu Sai-nan,et al.  Optimization problem for AGV in automated warehouse system , 2008, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics.

[3]  Nikos A. Vlassis,et al.  Multi-robot decision making using coordination graphs , 2003 .

[4]  António Paulo Moreira,et al.  Shop Floor Scheduling in a Mobile Robotic Environment , 2011, EPIA.

[5]  Martin W. P. Savelsbergh,et al.  Efficient Insertion Heuristics for Vehicle Routing and Scheduling Problems , 2004, Transp. Sci..

[6]  Michel Gendreau,et al.  A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows , 2013, Comput. Oper. Res..

[7]  M.B.M. de Koster,et al.  Intelligent Control of Vehicle-Based Internal Transport Systems , 2001 .

[8]  Ling Qiu,et al.  Scheduling and routing algorithms for AGVs: A survey , 2002 .

[9]  David Naso,et al.  Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Tuan Le-Anh Intelligent Control of Vehicle-Based Internal Transport Systems , 2005 .

[11]  Pierre Castagna,et al.  A performance-based structural policy for conflict-free routing of bi-directional automated guided vehicles , 2005, Comput. Ind..

[12]  Wei-Chang Yeh,et al.  Deadlock prediction and avoidance for zone-control AGVS , 1998 .

[13]  Gilbert Laporte,et al.  A Tabu Search Heuristic for the Vehicle Routing Problem , 1991 .

[14]  T. Mautor,et al.  Arcs-States Mdels for the Vehicle Routing Problems: New Improvement Methods , 2004 .

[15]  Ronald D. Armstrong,et al.  Single machine scheduling to minimize mean absolute lateness: A heuristic solution , 1990, Comput. Oper. Res..

[16]  H. A. Pak,et al.  Heuristical job allocation in a flexible manufacturing system , 1986 .

[17]  Zdenko Kovacic,et al.  Time Windows Based Dynamic Routing in Multi-AGV Systems , 2010, IEEE Transactions on Automation Science and Engineering.

[18]  Antonio Sedeño-Noda,et al.  On the K shortest path trees problem , 2010, Eur. J. Oper. Res..

[19]  S. Kumanan,et al.  TASK SCHEDULING OF AGV IN FMS USING NON-TRADITIONAL OPTIMIZATION TECHNIQUES , 2010 .

[20]  Ihsan Sabuncuoglu,et al.  Analysis of reactive scheduling problems in a job shop environment , 2000, Eur. J. Oper. Res..

[21]  Cristian Secchi,et al.  TRAFCON – Traffic Control of AGVs in Automatic Warehouses , 2014 .

[22]  X T S C H A N,et al.  A Comprehensive Survey and Future Trend of Simulation Study on Fms Scheduling , 2022 .

[23]  Michel Gendreau,et al.  An exact algorithm for the elementary shortest path problem with resource constraints: Application to some vehicle routing problems , 2004, Networks.