Lab Meeting November 15, 2010 (fish60): Unfreezing the Robot: Navigation in Dense, Interacting Crowds

Title: Unfreezing the Robot: Navigation in Dense, Interacting Crowds(IROS 2010)
Author: Peter Trautman and Andreas Krause

Abstract—In this paper, we study the safe navigation of a mobile robot through crowds of dynamic agents with uncertain trajectories. Existing algorithms suffer from the “freezing robot” problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary aneuvers) to avoid collisions. ... In this work, we demonstrate that both the individual prediction and the predictive uncertainty have little to do with the frozen robot problem. Our key insight is that dynamic agents solve the frozen robot problem by engaging in “joint collision avoidance”: They cooperatively make room to create feasible trajectories. We develop IGP, a nonparametric statistical model based on Dependent Output Gaussian Processes that can estimate crowd interaction from data. Our model naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation. We then show how planning in this model can be efficiently implemented using particle based inference.

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