Open AccessProceedings Article
Planning under Uncertainty for Reliable Health Care Robotics
Nicholas Roy,Geoffrey J. Gordon,Sebastian Thrun +2 more
- 01 Jan 2003
pp 417-426
TL;DR: An algorithm for representing real world POMDP problems compactly is demonstrated, which is able to find moving people in close to optimal time, where the optimal policy would start with knowledge of the person’s location.
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Abstract: We describe a mobile robot system, designed to assist residents of an retirement facility. This system is being developed to respond to an aging population and a predicted shortage of nursing professionals. In this paper, we discuss the task of finding and escorting people from place to place in the facility, a task containing uncertainty throughout the problem. Planning algorithms that model uncertainty well such as Partially Observable Markov Decision Processes (POMDPs) do not scale tractably to most real world problems. We demonstrate an algorithm for representing real world POMDP problems compactly, which allows us to find good policies in reasonable amounts of time. We show that our algorithm is able to find moving people in close to optimal time, where the optimal policy would start with knowledge of the person’s location.
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Citations
•Book
A Concise Introduction to Decentralized POMDPs
Frans A. Oliehoek,Christopher Amato +1 more
- 03 Jun 2016
TL;DR: This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs).
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- 03 Dec 2010
TL;DR: IGP is developed, a nonparametric statistical model based on dependent output Gaussian processes that can estimate crowd interaction from data that naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation.
A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control
TL;DR: In this paper, the authors present a method for chance-constrained predictive stochastic control of dynamic systems, which takes into account uncertainty to ensure that the probability of failure due to collision with obstacles, for example, is below a given threshold.
Robot navigation in dense human crowds: Statistical models and experimental studies of human-robot cooperation
TL;DR: It is concluded that a cooperation model is critical for safe and efficient robot navigation in dense human crowds and the salient characteristics of nearly any dynamic navigation algorithm.
Robot Motion Planning in Dynamic, Uncertain Environments
Noel E. Du Toit,J.W. Burdick +1 more
TL;DR: To approximately solve the stochastic dynamic programming problem that is associated with DUE planning, a partially closed-loop receding horizon control algorithm is presented whose solution integrates prediction, estimation, and planning while also accounting for chance constraints that arise from the uncertain locations of the robot and obstacles.
281
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