Data

Tools

Indexes

Applications

Experiments

Open Source Science

multiagenttrajectoryplanning

Experiments of the "Multi-Agent Trajectory Planning with NUV Priors" paper

https://github.com/biaslab/multiagenttrajectoryplanning

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 2 committers (50.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.5%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

Experiments of the "Multi-Agent Trajectory Planning with NUV Priors" paper

Basic Info
  • Host: GitHub
  • Owner: biaslab
  • License: mit
  • Language: Julia
  • Default Branch: main
  • Homepage:
  • Size: 1.38 MB
Statistics
  • Stars: 11
  • Watchers: 3
  • Forks: 3
  • Open Issues: 1
  • Releases: 0
Created over 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

Multi-Agent Trajectory Planning with NUV Priors

By Bart van Erp, Dmitry Bagaev, Albert Podusenko Ismail Senoz and Bert de Vries

Abstract

This paper presents a probabilistic model-based approach to centralized multi-agent trajectory planning. This approach allows for incorporating uncertainty of the state and dynamics of the agents directly in the model. Probabilistic inference is then efficiently automated using message passing. The recently introduced normal-with-unknown-variance (NUV) priors are used to prevent collisions between agents and obstacles. Furthermore, a new expectation-maximization inference scheme is presented for box and halfspace priors, which takes state uncertainty into account when avoiding collisions.

This repository contains all experiments of the paper.

Installation instructions

  1. Install Julia

  2. activate environment (using ] and backspace you can switch between the regular prompt and package manager) ```julia

    ] activate . ```

  3. instantiate environment (only required once) ```julia

    ] instantiate ```

  4. start Pluto ```julia

    using Pluto; Pluto.run() ```

License

MIT License Copyright (c) 2023 BIASlab

Owner

  • Name: BIASlab
  • Login: biaslab
  • Kind: organization
  • Email: info@biaslab.org
  • Location: Eindhoven, the Netherlands

Bayesian Intelligent Autonomous Systems lab

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "Please cite this research as below."
authors:
- family-names: "van Erp"
  given-names: "Bart"
  orcid: "https://orcid.org/0000-0002-5619-7071"
- family-names: "Bagaev"
  given-names: "Dmitry"
- family-names: "Podusenko"
  given-names: "Albert"
- family-names: "Senoz"
  given-names: "Ismail"
- family-names: "de Vries"
  given-names: "Bert"
title: "Multi-Agent Trajectory Planning with NUV Priors"
url: "https://github.com/biaslab/MultiAgentTrajectoryPlanning"
preferred-citation:
  authors:
  - family-names: "van Erp"
    given-names: "Bart"
    orcid: "https://orcid.org/0000-0002-5619-7071"
  - family-names: "Bagaev"
    given-names: "Dmitry"
  - family-names: "Podusenko"
    given-names: "Albert"
  - family-names: "Senoz"
    given-names: "Ismail"
  - family-names: "de Vries"
    given-names: "Bert"
  conference:
    name: "American Control Conference 2024"
  type: generic
  title: "Multi-Agent Trajectory Planning with NUV Priors"
  year: 2024

GitHub Events

Total
  • Issues event: 5
  • Watch event: 5
  • Issue comment event: 11
  • Fork event: 1
Last Year
  • Issues event: 5
  • Watch event: 5
  • Issue comment event: 11
  • Fork event: 1

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 20
  • Total Committers: 2
  • Avg Commits per committer: 10.0
  • Development Distribution Score (DDS): 0.05
Past Year
  • Commits: 3
  • Committers: 1
  • Avg Commits per committer: 3.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Bart van Erp b****p@t****l 19
Bagaev Dmitry b****i@g****m 1
Committer Domains (Top 20 + Academic)
tue.nl: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 5
  • Total pull requests: 2
  • Average time to close issues: 16 days
  • Average time to close pull requests: about 1 hour
  • Total issue authors: 3
  • Total pull request authors: 2
  • Average comments per issue: 6.2
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 3
  • Pull requests: 0
  • Average time to close issues: 5 days
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 4.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Flawless1202 (2)
  • Michi-Tsubaki (2)
  • zdx3578 (1)
Pull Request Authors
  • bartvanerp (1)
  • bvdmitri (1)
Top Labels
Issue Labels
Pull Request Labels