Experimental Validation of a Multirobot Distributed Receding Horizon Motion Planning Approach

José Mendes Filho 1, 2 Eric Lucet 1 David Filliat 2, 3
1 LRI - Laboratoire de Robotique Interactive
DIASI - Département Intelligence Ambiante et Systèmes Interactifs : DRT/LIST/DIASI
3 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : This paper addresses the problem of motion planning for a multirobot system in a partially known environment where conditions such as uncertainty about robots' positions and communication delays are real. In particular, we detail the use of a Distributed Receding Horizon Approach that guarantees collision avoidance with static obstacles and between robots communicating with each other. Underlying optimization problems are solved by using a Sequential Least Squares Programming algorithm. Experiments with real nonholonomic mobile platforms are performed. The proposed framework is compared with the Dynamic Window approach to motion planning in a single robot setup. A second experiment shows results for a multirobot case using two robots where collision is avoided even in presence of significant localization uncertainties.
Document type :
Conference papers
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01935322
Contributor : David Filliat <>
Submitted on : Monday, November 26, 2018 - 3:58:11 PM
Last modification on : Friday, February 8, 2019 - 11:04:04 AM
Document(s) archivé(s) le : Wednesday, February 27, 2019 - 2:34:01 PM

File

MendesFilho_experimental-valid...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01935322, version 1

Citation

José Mendes Filho, Eric Lucet, David Filliat. Experimental Validation of a Multirobot Distributed Receding Horizon Motion Planning Approach. ICARCV 2018 - 15th International Conference on Control, Automation, Robotics and Vision, Nov 2018, Singapour, Singapore. ⟨hal-01935322⟩

Share

Metrics

Record views

74

Files downloads

83