Dynamic Path Planning and Movement Control in Pedestrian Simulation

Modeling and simulation of pedestrian behavior is used in applications such as planning large buildings, disaster management, or urban planning. Realistically simulating pedestrian behavior is challenging, due to the complexity of individual behavior as well as the complexity of interactions of pedestrians with each other and with the environment. This work-in-progress paper addresses the tactical (path planning) and the operational level (movement control) of pedestrian simulation from the perspective of multiagent-based modeling. We propose (1) an novel extension of the JPS routing algorithm for tactical planning, and (2) an architecture how path planning can be integrated with a social-force based movement control. The architecture is inspired by layered architectures for robot planning and control. We validate correctness and efficiency of our approach through simulation runs.

[1]  Arnold Neumaier,et al.  Introduction to Numerical Analysis , 2001 .

[2]  Jan Willemson,et al.  Path Selection for Mobile Robots in Dynamic Environments , 2013 .

[3]  F. Cherif,et al.  Crowd simulation influenced by agent's socio-psychological state , 2010, ArXiv.

[4]  Uwe Aickelin,et al.  Introduction to Multi-Agent Simulation , 2008, ArXiv.

[5]  Cherif Foudil,et al.  Path finding and Collision Avoidance in Crowd Simulation , 2009, J. Comput. Inf. Technol..

[6]  Ian Millington,et al.  Artificial Intelligence for Games , 2006, The Morgan Kaufmann series in interactive 3D technology.

[7]  Erann Gat,et al.  Integrating Planning and Reacting in a Heterogeneous Asynchronous Architecture for Controlling Real-World Mobile Robots , 1992, AAAI.

[8]  Jörg P. Müller,et al.  The Design of Intelligent Agents , 1996, Lecture Notes in Computer Science.

[9]  Alban Grastien,et al.  Improving Jump Point Search , 2014, ICAPS.

[10]  Lubos Buzna,et al.  Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions , 2005, Transp. Sci..

[11]  Michel Fremond,et al.  Collisions Engineering: Theory and Applications , 2016 .

[12]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.

[13]  Steven Rabin,et al.  Game AI Pro 2: Collected Wisdom of Game AI Professionals , 2013 .

[14]  Michael Wooldridge,et al.  Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology) , 2007 .

[15]  Tsai-Yen Li,et al.  Motion planning for a crowd of robots , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[16]  Richard E. Korf,et al.  Depth-First Iterative-Deepening: An Optimal Admissible Tree Search , 1985, Artif. Intell..

[17]  Dirk Helbing,et al.  Crowd disasters as systemic failures: analysis of the Love Parade disaster , 2012, EPJ Data Science.

[18]  Jörg P. Müller,et al.  LightJason - A BDI Framework Inspired by Jason , 2016, EUMAS/AT.

[19]  Alban Grastien,et al.  Online Graph Pruning for Pathfinding On Grid Maps , 2011, AAAI.

[20]  Mohd Shahrizal Sunar,et al.  A Comprehensive Study on Pathfinding Techniques for Robotics and Video Games , 2015, Int. J. Comput. Games Technol..

[21]  Paulo Leitão,et al.  Parallelising Multi-agent Systems for High Performance Computing , 2013 .

[22]  Lorenza Manenti,et al.  Towards Modeling Activity Scheduling in an Agent-based Model for Pedestrian Dynamics Simulation , 2012, WOA.

[23]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[24]  Stefania Bandini,et al.  Crowd Behavior Modeling: From Cellular Automata to Multi-Agent Systems , 2009, Multi-Agent Systems.

[25]  T. Vicsek,et al.  Simulation of pedestrian crowds in normal and evacuation situations , 2002 .

[26]  Neil A. Dodgson,et al.  Psychologically-based vision and attention for the simulation of human behaviour , 2005, GRAPHITE.

[27]  Craig W. Reynolds Steering Behaviors For Autonomous Characters , 1999 .

[28]  Stefania Bandini,et al.  When reactive agents are not enough: Tactical level decisions in pedestrian simulation , 2015, Intelligenza Artificiale.

[29]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[30]  Jorg P. Muller,et al.  The Design of Intelligent Agents: A Layered Approach , 1996 .