Proving Feasibility of a Docking Mission: A Contractor Programming Approach

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 appro...

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Bibliographic Details
Main Authors: Bourgois, A. (Author), Jaulin, L. (Author), Rauh, A. (Author), Rohou, S. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Online Access:View Fulltext in Publisher
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001 10.3390-math10071130
008 220425s2022 CNT 000 0 und d
020 |a 22277390 (ISSN) 
245 1 0 |a Proving Feasibility of a Docking Mission: A Contractor Programming Approach 
260 0 |b MDPI  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.3390/math10071130 
520 3 |a 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. 
650 0 4 |a constraint programming 
650 0 4 |a guaranteed integration 
650 0 4 |a interval analysis 
650 0 4 |a tube arithmetic 
650 0 4 |a underwater robotics applications 
700 1 |a Bourgois, A.  |e author 
700 1 |a Jaulin, L.  |e author 
700 1 |a Rauh, A.  |e author 
700 1 |a Rohou, S.  |e author 
773 |t Mathematics