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Robot Navigation in Dense Crowds: St...

Trautman, Peter.

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Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation./
作者:	Trautman, Peter.
面頁冊數:	134 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:	Dissertation Abstracts International74-09B(E).
標題:	Engineering, Robotics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3562863
ISBN:	9781303107009

Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation.
Trautman, Peter.

Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation. - 134 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--California Institute of Technology, 2013.

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.

ISBN: 9781303107009Subjects--Topical Terms:

1018454
Engineering, Robotics.

Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation.
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245 1 0 $a Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation.
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500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Adviser: Richard M. Murray.
502 $a Thesis (Ph.D.)--California Institute of Technology, 2013.
520 $a 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.
520 $a 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.
520 $a 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.
590 $a School code: 0037.
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710 2 $a California Institute of Technology. $b Control and Dynamical Systems. $3 2093108
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791 $a Ph.D.
792 $a 2013
793 $a English
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