Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty. Marcon dos Santos, G. Ph.D. Thesis, Oregon State University, June, 2020.
Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty [link]Paper  abstract   bibtex   2 downloads  
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.
@phdthesis{MarcondosSantos2020e,
  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.},
  added-at = {2020-06-15T20:19:59.000+0200},
  author = {Marcon dos Santos, Gilberto},
  biburl = {https://www.bibsonomy.org/bibtex/2d39781ac507bd01f8fe6929410b2dc39/marcondg},
  interhash = {f489c500a996062202c9dedec50bea46},
  intrahash = {d39781ac507bd01f8fe6929410b2dc39},
  keywords = {mine},
  month = {June},
  school = {Oregon State University},
  timestamp = {2020-06-15T20:22:12.000+0200},
  title = {Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty},
  url = {https://ir.library.oregonstate.edu/downloads/gb19fd28x},
  year = 2020
}
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