Proving Feasibility of a Docking Mission: A Contractor Programming Approach

Auguste Bourgois, Simon Rohou, Luc Jaulin and Andreas Rauh
Additional contact information
Auguste Bourgois: Forssea Robotics, 130 Rue de Lourmel, 75015 Paris, France
Simon Rohou: Lab-STICC, ENSTA Bretagne, 2 rue François Verny, 29200 Brest, France
Luc Jaulin: Lab-STICC, ENSTA Bretagne, 2 rue François Verny, 29200 Brest, France
Andreas Rauh: Group: Distributed Control in Interconnected Systems, Department of Computing Science, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany

Mathematics, 2022, vol. 10, issue 7, 1-20

Abstract: Recent advances in computational power, algorithms, and sensors allow robots to perform complex and dangerous tasks, such as autonomous missions in space or underwater. Given the high operational costs, simulations are run beforehand to predict the possible outcomes of a mission. However, this approach is limited as it is based on parameter space discretization and therefore cannot be considered a proof of feasibility. To overcome this limitation, set-membership methods based on interval analysis, guaranteed integration, and contractor programming have proven their efficiency. Guaranteed integration algorithms can predict the possible trajectories of a system initialized in a given set in the form of tubes of trajectories. The contractor programming consists in removing the trajectories violating predefined constraints from a system’s tube of possible trajectories. Our contribution consists in merging both approaches to allow for the usage of differential constraints in a contractor programming framework. We illustrate our method through examples related to robotics. We also released an open-source implementation of our algorithm in a unified library for tubes, allowing one to combine it with other constraints and increase the number of possible applications.

Keywords: interval analysis; constraint programming; guaranteed integration; underwater robotics applications; tube arithmetic (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/7/1130/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/7/1130/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:7:p:1130-:d:785134

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().