Graduate Thesis Or Dissertation
 

Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty

公开 Deposited

可下载的内容

下载PDF文件
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/j3860f12w

Descriptions

Attribute NameValues
Creator
Abstract
  • This dissertation incorporates coalition formation and probabilistic planning towards a domain-independent automated planning solution scalable to multiple heterogeneous robots in complex domains. The first research direction investigates the effectiveness of Task Fusion and introduces heuristics that improve task allocation and result in better quality plans, while requiring lower computational cost than the baseline approaches. The heuristics incorporate relaxed plans to estimate coupling and determine which tasks to fuse. As a result, larger temporal continuous planning problems involving multiple robots can be solved. The second research direction introduces new coordination methods to merge plans and resolve conflicts while extending the framework to domains with stochastic action duration. Merging distributedly generated plans becomes computationally costly when task plans are tightly coupled, and conflicts arise due to dependencies between plan actions. Existing methods either scale poorly as the number of agents and tasks increases, or do not minimize makespan, the overall time necessary to execute all tasks. A new family of plan coordination and conflict resolution algorithms is introduced to merge independently generated plans, minimize the resulting makespan, and scale to a large number of tasks and agents in complex problems. A thorough algorithmic analysis and empirical evaluation demonstrates how the new conflict identification and resolution models can impact the resulting plan quality and computational cost across three heterogeneous multiagent domains and outperform the baseline algorithms.
License
Resource Type
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
权利声明
Funding Statement (additional comments about funding)
  • This work was partially supported by NSF grant #1723924.
Publisher
Peer Reviewed
Language

关联

Parents:

This work has no parents.

属于 Collection:

单件

缩略图 标题 上传日期 公开度 行动
file details MarconDosSantosG2020.pdf 2020-06-11 公开 下载