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Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation

Citation

Trautman, Peter (2013) Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:05182013-191132413

Abstract

This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called 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 maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m2, while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m2. We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Robot navigation; Gaussian Processes; human robot interaction; human robot cooperation
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Control and Dynamical Systems
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Murray, Richard M. (advisor)
  • Krause, R. Andreas (advisor)
  • Burdick, Joel Wakeman (advisor)
Thesis Committee:
  • Murray, Richard M. (chair)
  • Burdick, Joel Wakeman
  • Beck, James L.
  • Krause, R. Andreas
Defense Date:28 April 2012
Non-Caltech Author Email:peter.trautman (AT) gmail.com
Funders:
Funding AgencyGrant Number
BoeingCT-BA-GTA
Record Number:CaltechTHESIS:05182013-191132413
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:05182013-191132413
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:7724
Collection:CaltechTHESIS
Deposited By: Peter Trautman
Deposited On:22 May 2013 22:22
Last Modified:13 Apr 2015 19:54

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