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Online Trajectory Planning Algorithms for Robotic Systems Under Uncertainty in Interactive Environments.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Online Trajectory Planning Algorithms for Robotic Systems Under Uncertainty in Interactive Environments./
Author:	Nishimura, Haruki.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	168 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
Subject:	Study abroad. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812874
ISBN:	9798494455956

Online Trajectory Planning Algorithms for Robotic Systems Under Uncertainty in Interactive Environments.
Nishimura, Haruki.

Online Trajectory Planning Algorithms for Robotic Systems Under Uncertainty in Interactive Environments. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 168 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

The mission of this thesis is to develop algorithms for planning and control of intelligent mobile robots that operate autonomously in open, interactive environments. Presence of other agents and objects in such an environment makes planning significantly challenging, as they inevitably bring about environmental and dynamic uncertainty that the robot must properly handle. Despite recent advances in perception, planning and control, many existing robotic systems to date lack the capability to consider and address uncertainty, which demands that the robots be caged or confined to a dedicated, structured workspace. For example, success of thousands of mobile robots nowadays deployed in logistics centers is heavily reliant on their closed and controlled operating environments. In this thesis, we propose a series of computationally efficient algorithms that can collectively overcome uncertainty of various sources towards reliable autonomy for "cage-free" robotic operations. The methods presented in the thesis leverage probability theory to quantify the amount of present and future uncertainty. Based on the quantification, we develop planning and control algorithms that either mitigate, avoid the risk of, or are robust against uncertainty so that the robot can successfully accomplish a given task. We take a model-based approach in developing those algorithms, which allows us to exploit physical properties of dynamical systems and onboard sensors when possible. Another crucial aspect of the proposed methods is their online nature, meaning that control signals are computed in situ based on the currently available information. This is enabled by fast, efficient computation of our algorithms, and is advantageous in that the robot can quickly react to rapidly changing environments. In the first part of the thesis, we address challenges associated with state uncertainty, which represents unknowns about the current state of the system of interest. This can include unknown intent of other interacting agents, or positions of targets to locate. We propose and employ recursive Bayesian inference frameworks to keep track of evolving state uncertainty over time. The proposed planning algorithms further assist the inference frameworks to actively mitigate state uncertainty as appropriate, so that the robot can execute suitable control actions with certainty. We leverage tools from sequential decision-making and optimal control to develop those algorithms. We demonstrate the effectiveness of our approach in a multitude of tasks that involve state uncertainty, with different combinations of dynamical systems and sensing modalities. This includes vision-based active intent inference, active target tracking with range-only observations, and simultaneous object manipulation and parameter estimation. We then turn our attention to transition uncertainty, which governs the unpredictability of future states of the system. We especially focus on safety-critical problems where transition uncertainty must not be ignored. For instance, a robot navigating in close proximity to humans has to carefully perform planning so that collisions are avoided with high confidence. We take a risk-aware planning approach, in which a risk metric that takes into account the variance of uncertainty is to be optimized.

ISBN: 9798494455956Subjects--Topical Terms:

3557623
Study abroad.

Online Trajectory Planning Algorithms for Robotic Systems Under Uncertainty in Interactive Environments.
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300 $a 168 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Includes supplementary digital materials.
500 $a Advisor: Schwager, Mac;Gao, Grace;Kochenderfer, Mykel.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The mission of this thesis is to develop algorithms for planning and control of intelligent mobile robots that operate autonomously in open, interactive environments. Presence of other agents and objects in such an environment makes planning significantly challenging, as they inevitably bring about environmental and dynamic uncertainty that the robot must properly handle. Despite recent advances in perception, planning and control, many existing robotic systems to date lack the capability to consider and address uncertainty, which demands that the robots be caged or confined to a dedicated, structured workspace. For example, success of thousands of mobile robots nowadays deployed in logistics centers is heavily reliant on their closed and controlled operating environments. In this thesis, we propose a series of computationally efficient algorithms that can collectively overcome uncertainty of various sources towards reliable autonomy for "cage-free" robotic operations. The methods presented in the thesis leverage probability theory to quantify the amount of present and future uncertainty. Based on the quantification, we develop planning and control algorithms that either mitigate, avoid the risk of, or are robust against uncertainty so that the robot can successfully accomplish a given task. We take a model-based approach in developing those algorithms, which allows us to exploit physical properties of dynamical systems and onboard sensors when possible. Another crucial aspect of the proposed methods is their online nature, meaning that control signals are computed in situ based on the currently available information. This is enabled by fast, efficient computation of our algorithms, and is advantageous in that the robot can quickly react to rapidly changing environments. In the first part of the thesis, we address challenges associated with state uncertainty, which represents unknowns about the current state of the system of interest. This can include unknown intent of other interacting agents, or positions of targets to locate. We propose and employ recursive Bayesian inference frameworks to keep track of evolving state uncertainty over time. The proposed planning algorithms further assist the inference frameworks to actively mitigate state uncertainty as appropriate, so that the robot can execute suitable control actions with certainty. We leverage tools from sequential decision-making and optimal control to develop those algorithms. We demonstrate the effectiveness of our approach in a multitude of tasks that involve state uncertainty, with different combinations of dynamical systems and sensing modalities. This includes vision-based active intent inference, active target tracking with range-only observations, and simultaneous object manipulation and parameter estimation. We then turn our attention to transition uncertainty, which governs the unpredictability of future states of the system. We especially focus on safety-critical problems where transition uncertainty must not be ignored. For instance, a robot navigating in close proximity to humans has to carefully perform planning so that collisions are avoided with high confidence. We take a risk-aware planning approach, in which a risk metric that takes into account the variance of uncertainty is to be optimized.
590 $a School code: 0212.
650 4 $a Study abroad. $3 3557623
650 4 $a Control theory. $3 535880
650 4 $a Robots. $3 529507
650 4 $a Cameras. $3 524039
650 4 $a Algorithms. $3 536374
650 4 $a Kalman filters. $3 3682571
650 4 $a Planning. $3 552734
650 4 $a Robotics. $3 519753
650 4 $a Computer science. $3 523869
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710 2 $a Stanford University. $3 754827
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