Proxemic Group Behaviors

University Of North Carolina at Chapel Hill


We present a decentralized algorithm for group based coherent and reciprocal multi-agent navigation. In addition to generating collision-free trajectories for each agent, our approach is able to simulate macroscopic group movements and proxemic behaviors that result in coherent navigation. Our approach is general, makes no assumption about the size or shape of the group, and can generate smooth trajectories for the agents. Furthermore, it can dynamically adapt to the obstacles or behavior of other agents. The additional overhead of generating proxemic group behaviors is relatively small and our approach can simulate hundreds of agents in real-time. We highlight its benefits on different benchmarks.


Liang He, Jia Pan, Wenping Wang and Dinesh Manocha Proxemic Group Behaviors using Reciprocal Multi-Agent Navigation PDF: To appear in ICRA 2016





We present a new algorithm to simulate dynamic group behaviors for interactive multi-agent crowd simulation. Our approach is general and makes no assumption about the environment, shape, or size of the groups.We use the least effort principle to perform coherent group navigation and present efficient inter-group and intra-group maintenance techniques. We extend the reciprocal collision avoidance scheme to perform agent-group and group-group collision avoidance that can generate collision-free and coherent trajectories. The additional overhead of dynamic group simulation is relatively small. We highlight its interactive performance in complex scenarios with hundreds of agents and highlight its benefits over prior methods.

Liang He. Jia Pan. Sahil Narang and Dinesh Manocha Dynamic Group Behaviors for Interactive Crowd Simulation PDF: To appear in ACM SIGGRAPH Symposium on Computer Animation (SCA), ETH Zurich, Switzerland 2016