Robust Space-time Footsteps for Agent-based Steering

Randomized, continuous footstep action space.

Geometric validation to ensure valid configuration.

With this work we build upon prevous work to inductavely design a robust footsteps-based steering algorithm. The steering algorithm uses an A* algorithm to generate least cost footstep plans between goals while avoiding dynamic collisions.

Abstract

A number of recent agent-based steering methods abandon the standard particle abstraction of an agent's locomotion abilities, and employ more complex models from timed footsteps to physics-based controllers. These models often provide the action space of an optimal search method that plans a sequence of steering actions for each agent that minimize a performance criterion. The transition from particle based models to more complex ones is not straightforward and gives rise to a number of technical challenges. For example, a particle representation is constant, symmetric and convex, while a footstep model maybe non-convex and dynamic. In this paper, we discuss our recent experience with a few of specific issues, and how we addressed them in the context of space-time footstep planning for steering virtual humans.

Recieved CASA2015 Best Short Paper Award

Photo - From Left to Right: Glen Berseth and Prof Jian Jun Zhang

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