國立虎尾科技大學 |

Collaborative Multi-Robot Autonomy Via Distributed Optimization.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Collaborative Multi-Robot Autonomy Via Distributed Optimization./
Author:	Shorinwa, Ola.
Description:	1 online resource (229 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Transportation. -
Online resource:	click for full text (PQDT)
ISBN:	9798382641263

Collaborative Multi-Robot Autonomy Via Distributed Optimization.
Shorinwa, Ola.

Collaborative Multi-Robot Autonomy Via Distributed Optimization. - 1 online resource (229 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

The high complexity of real-world tasks necessitates the deployment of multi-robot teams, equipped with different onboard sensors and capabilities. Typically, no robot has all the capabilities required to complete the specified task; as a result, the multi-robot team must collaboratetogether to realize the shared objective.This thesis presents distributed algorithms that enable multi-robot teams to operate autonomously while working collaboratively. Specifically, we leverage distributed optimization to derive efficient algorithms that enable multi-robot autonomy in core robotics applications, such as autonomous transportation, search-and-rescue problems, and collaborative object manipulation. Our algorithms are fully-distributed (i.e., our algorithms do not require a central node (station) for any computation or coordination). Rather, each robot communicates with its one-hop(immediate) neighbors over a peer-to-peer communication network.We introduce a distributed optimization algorithm for problems consisting of optimization variables that can be partitionedamong the robots. The proposed algorithm enables robots to avoid unnecessary optimization over irrelevant problem variables, resulting in greater computation and communication efficiency. Problems of this form are prevalent in the field of robotics, e.g., trajectory optimization. We present specialized algorithms for contact-implicit trajectory optimization and model predictive control. Our distributed contact-implicit optimization algorithm enables each robot to make and break contact, as required, to perform complex object manipulation tasks through narrow areas. Further, our distributed algorithm for model predictive control enables each robot to compute the required forces and torques needed to successfully complete a specified multi-robot task. Our algorithms eliminate the need for each robot to compute the torques of other robots, which may not be directly relevant to its performance on the given task. In addition, we present distributed algorithms for multi-robot target tracking and task assignment. Through maximum a-posteriori optimization, we derive a distributed trajectory estimation algorithm that enables each robot to estimate the trajectory of the target without sharing its local (potentially high-dimensional) observations. In addition, we introduce three algorithms for the multi-robot task assignment problem. These algorithms are designed for a broad class of multi-robot systems, considering the broad range of computation and communication resources available to multi-robot teams. In our distributed algorithms, each robot does not share its local problem data (including its local observations) and its objective and constraint functions. Rather, each robot communicates its local problem variable, which is of a generally lower-dimension, reducing the computation and communication overhead associated with our algorithms.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382641263Subjects--Topical Terms:

558117
Transportation.
Index Terms--Genre/Form:

554714
Electronic books.

Collaborative Multi-Robot Autonomy Via Distributed Optimization.
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502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a The high complexity of real-world tasks necessitates the deployment of multi-robot teams, equipped with different onboard sensors and capabilities. Typically, no robot has all the capabilities required to complete the specified task; as a result, the multi-robot team must collaboratetogether to realize the shared objective.This thesis presents distributed algorithms that enable multi-robot teams to operate autonomously while working collaboratively. Specifically, we leverage distributed optimization to derive efficient algorithms that enable multi-robot autonomy in core robotics applications, such as autonomous transportation, search-and-rescue problems, and collaborative object manipulation. Our algorithms are fully-distributed (i.e., our algorithms do not require a central node (station) for any computation or coordination). Rather, each robot communicates with its one-hop(immediate) neighbors over a peer-to-peer communication network.We introduce a distributed optimization algorithm for problems consisting of optimization variables that can be partitionedamong the robots. The proposed algorithm enables robots to avoid unnecessary optimization over irrelevant problem variables, resulting in greater computation and communication efficiency. Problems of this form are prevalent in the field of robotics, e.g., trajectory optimization. We present specialized algorithms for contact-implicit trajectory optimization and model predictive control. Our distributed contact-implicit optimization algorithm enables each robot to make and break contact, as required, to perform complex object manipulation tasks through narrow areas. Further, our distributed algorithm for model predictive control enables each robot to compute the required forces and torques needed to successfully complete a specified multi-robot task. Our algorithms eliminate the need for each robot to compute the torques of other robots, which may not be directly relevant to its performance on the given task. In addition, we present distributed algorithms for multi-robot target tracking and task assignment. Through maximum a-posteriori optimization, we derive a distributed trajectory estimation algorithm that enables each robot to estimate the trajectory of the target without sharing its local (potentially high-dimensional) observations. In addition, we introduce three algorithms for the multi-robot task assignment problem. These algorithms are designed for a broad class of multi-robot systems, considering the broad range of computation and communication resources available to multi-robot teams. In our distributed algorithms, each robot does not share its local problem data (including its local observations) and its objective and constraint functions. Rather, each robot communicates its local problem variable, which is of a generally lower-dimension, reducing the computation and communication overhead associated with our algorithms.
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