Petri Net Toolbox for Multi-Robot Planning under Uncertainty

Currently, there is a lack of developer-friendly software tools to formally address multi-robot coordination problems and obtain robust, efficient, and predictable strategies. This paper introduces a software toolbox that encapsulates, in one single package, modeling, planning, and execution algorit...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2021-12, Vol.11 (24), p.12087
Main Authors: Azevedo, Carlos, Matos, António, Lima, Pedro U., Avendaño, Jose
Format: Article
Language:English
Subjects:
Algorithms
Computer applications
Computer programs
Design
generalized stochastic Petri nets with rewards
heterogeneous multi-robot systems
Inspection
Markov analysis
multi-robot coordination
multi-robot systems
Multiple robots
Petri net toolbox
Petri nets
plan execution
Robots
Software
Software development tools
Software utilities
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Currently, there is a lack of developer-friendly software tools to formally address multi-robot coordination problems and obtain robust, efficient, and predictable strategies. This paper introduces a software toolbox that encapsulates, in one single package, modeling, planning, and execution algorithms. It implements a state-of-the-art approach to representing multi-robot systems: generalized Petri nets with rewards (GSPNRs). GSPNRs enable capturing multiple robots, decision states, action execution states and respective outcomes, action duration uncertainty, and team-level objectives. We introduce a novel algorithm that simplifies the model design process as it generates a GSPNR from a topological map. We also introduce a novel execution algorithm that coordinates the multi-robot system according to a given policy. This is achieved without compromising the model compactness introduced by representing robots as indistinguishable tokens. We characterize the computational performance of the toolbox with a series of stress tests. These tests reveal a lightweight implementation that requires low CPU and memory usage. We showcase the toolbox functionalities by solving a multi-robot inspection application, where we extend GSPNRs to enable the representation of heterogeneous systems and system resources such as battery levels and counters.
ISSN:2076-3417
2076-3417
DOI:10.3390/app112412087