Behavioral Spherical Harmonics for Long-Range Agents' Interaction

We introduce behavioral spherical harmonic (BSH), a novel approach to efficiently and compactly represent the directional-dependent behavior of agent. BSH is based on spherical harmonics to project the directional information of a group of multiple agents to a vector of few coefficients; thus, BSH drastically reduces the complexity of the directional evaluation, as it requires only few agent-group interactions instead of multiple agent-agent ones. We show how the BSH model can efficiently model intricate behaviors such as long-range collision avoidance, reaching interactive performance and avoiding agent congestion on challenging multi-groups scenarios.Furthermore, we demonstrate how both the innate parallelism and the compact coefficient representation of the BSH model are well suited for GPU architectures, showing performance analysis of our OpenCL implementation.