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
Published in:Mathematics (Basel) Vol. 10; no. 7; p. 1130
Main Authors: Bourgois, Auguste, Rohou, Simon, Jaulin, Luc, Rauh, Andreas
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2022
MDPI
Subjects:
Algorithms
Artificial Intelligence
Automatic
Computer Science
constraint programming
Contractors
Dynamical Systems
Engineering Sciences
Feasibility
Food science
guaranteed integration
interval analysis
Mathematics
Numerical Analysis
Power
Programming
Robotics
Robots
Sensors
Software
Space missions
Task complexity
tube arithmetic
Tubes
underwater robotics applications
underwater robotics applications
guaranteed integration
interval analysis
constraint programming
tube arithmetic
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math10071130