Robot Motion Planning
Populating virtual environments with crowds
Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications
Planning Algorithms
Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions
Transportation Science
Controlling individual agents in high-density crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphics and Animation)
Experiment-based modeling, simulation and validation of interactions between virtual walkers
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Behavior-based motion planning for group control
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Safe and Distributed Kinodynamic Replanning for Vehicular Networks
Mobile Networks and Applications
Independent navigation of multiple robots and virtual agents
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Proceedings of Motion on Games
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Many multi-agent applications may involve a notion of spatial coherence. For instance, simulations of virtual agents often need to model a coherent group or crowd. Alternatively, robots may prefer to stay within a pre-specified communication range. This paper proposes an extension of a decentralized, reactive collision avoidance framework, which defines obstacles in the velocity space, known as Velocity Obstacles (VOs), for coherent groups of agents. The extension, referred to in this work as a Loss of Communication Obstacle (LOCO), aims to maintain proximity among agents by imposing constraints in the velocity space and restricting the set of feasible controls. If the introduction of LOCOs results in a problem that is too restrictive, then the proximity constraints are relaxed in order to maintain collision avoidance. If agents break their proximity constraints, a method is applied to reconnect them. The approach is fast and integrates nicely with the Velocity Obstacle framework. It yields improved coherence for groups of robots connected through an input constraint graph that are moving with constant velocity. Simulated environments involving a single team moving among static obstacles, as well as multiple teams operating in the same environment, are considered in the experiments and evaluated for collisions, computational cost and proximity constraint maintenance. The experiments show that improved coherence is achieved while maintaining collision avoidance, at a small computational cost and path quality degradation.