Dario Izzo

Dario Izzo
European Space Agency | ESA · Advanced Concepts Team (ACT)

Ph.D., M.Sc.

About

261
Publications
75,983
Reads
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3,604
Citations
Additional affiliations
January 2005 - present
European Space Agency
Position
  • Scientific Coordinator
January 1999 - January 2005
Sapienza University of Rome
Position
  • Professor (Assistant)

Publications

Publications (261)
Preprint
Full-text available
Interpretable regression models are important for many application domains, as they allow experts to understand relations between variables from sparse data. Symbolic regression addresses this issue by searching the space of all possible free form equations that can be constructed from elementary algebraic functions. While explicit mathematical fun...
Preprint
Full-text available
Dyson spheres are hypothetical megastructures encircling stars in order to harvest most of their energy output. During the 11th edition of the GTOC challenge, participants were tasked with a complex trajectory planning related to the construction of a precursor Dyson structure, a heliocentric ring made of twelve stations. To this purpose, we develo...
Article
We present a novel approach for the detection of events in systems of ordinary differential equations. The new method combines the unique features of Taylor integrators with state-of-the-art polynomial root finding techniques to yield a novel algorithm ensuring strong event detection guarantees at a modest computational overhead. Detailed tests and...
Preprint
Full-text available
We present a novel approach for the detection of events in systems of ordinary differential equations. The new method combines the unique features of Taylor integrators with state-of-the-art polynomial root finding techniques to yield a novel algorithm ensuring strong event detection guarantees at a modest computational overhead. Detailed tests and...
Preprint
Full-text available
We train neural models to represent both the optimal policy (i.e. the optimal thrust direction) and the value function (i.e. the time of flight) for a time optimal, constant acceleration low-thrust rendezvous. In both cases we develop and make use of the data augmentation technique we call backward generation of optimal examples. We are thus able t...
Preprint
Full-text available
Numerical simulations are at the center of predicting the space debris environment of the upcoming decades. In light of debris generating events, such as continued anti-satellite weapon tests and planned mega-constellations, accurate predictions of the space debris environment are critical to ensure the long-term sustainability of critical satellit...
Preprint
Full-text available
Autonomous vision-based spaceborne navigation is an enabling technology for future on-orbit servicing and space logistics missions. While computer vision in general has benefited from Machine Learning (ML), training and validating spaceborne ML models are extremely challenging due to the impracticality of acquiring a large-scale labeled dataset of...
Preprint
Full-text available
Asteroids and other small bodies in the solar system tend to have irregular shapes, owing to their low gravity. This irregularity does not only apply to the topology, but also to the underlying geology, potentially containing regions of different densities and materials. The topology can be derived from optical observations, while the mass density...
Conference Paper
We study event-based sensors in the context of spacecraft guidance and control during a descent on Moon-like terrains. For this purpose, we develop a simulator reproducing the event-based camera outputs when exposed to synthetic images of a space environment. We find that it is possible to reconstruct, in this context, the divergence of optical flo...
Article
Multi-junction solar cells constitute the main source of power for space applications. However, exposure of solar cells to the space radiation environment significantly degrades their performance across the mission lifetime. Here, we seek to improve the radiation hardness of triple junction solar cell, GaInP/Ga(In)As/Ge, by decreasing the thickness...
Preprint
Full-text available
We present a novel approach based on artificial neural networks, so-called geodesyNets, and present compelling evidence of their ability to serve as accurate geodetic models of highly irregular bodies using minimal prior information on the body. The approach does not rely on the body shape information but, if available, can harness it. GeodesyNets...
Preprint
Full-text available
In advanced mission concepts with high levels of autonomy, spacecraft need to internally model the pose and shape of nearby orbiting objects. Recent works in neural scene representations show promising results for inferring generic three-dimensional scenes from optical images. Neural Radiance Fields (NeRF) have shown success in rendering highly spe...
Preprint
Full-text available
We present heyoka, a new, modern and general-purpose implementation of Taylor's integration method for the numerical solution of ordinary differential equations. Detailed numerical tests focused on difficult high-precision gravitational problems in astrodynamics and celestial mechanics show how our general-purpose integrator is competitive with and...
Article
We present heyoka, a new, modern and general-purpose implementation of Taylor’s integration method for the numerical solution of ordinary differential equations. Detailed numerical tests focused on difficult high-precision gravitational problems in astrodynamics and celestial mechanics show how our general-purpose integrator is competitive with and...
Article
Full-text available
Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can then plan risk mitigation measures. Such measures can be aided by the development of suitable machine learning (ML) models that pred...
Preprint
Full-text available
Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform the various operators who can then plan risk mitigation measures. Such measures could be aided by the development of suitable machine learning models predicti...
Article
Recent works have shown that the optimal statefeedback for deterministic, nonlinear autonomous systems, can be approximated by deep neural networks. In this work, we consider the stability of nonlinear systems controlled by such a network representation of the optimal feedback. First, we show that principal methods from stability theory readily app...
Conference Paper
Full-text available
In this paper, we combine the concepts of hyper-volume, ant colony optimization and nondominated sorting to develop a novel multi-objective ant colony optimizer for global space trajectory optimization. In particular, this algorithm is first tested on three space trajectory bi-objective test problems: an Earth-Mars transfer, an Earth-Venus transfer...
Article
Full-text available
Reliable pose estimation of uncooperative satellites is a key technology for enabling future on-orbit servicing and debris removal missions. The Kelvins Satellite Pose Estimation Challenge aims at evaluating and comparing monocular visionbased approaches and pushing the state-of-the-art on this problem. This work is based on the Satellite Pose Esti...
Preprint
One of the main and largely unexplored challenges in evolvingthe weights of neural networks using genetic algorithms is to finda sensible crossover operation between parent networks. Indeed,naive crossover leads to functionally damaged offspring that donot retain information from the parents. This is because neuralnetworks are invariant to permutat...
Preprint
Full-text available
We consider the Earth-Venus mass-optimal interplanetary transfer of a low-thrust spacecraft and show how the optimal guidance can be represented by deep networks in a large portion of the state space and to a high degree of accuracy. Imitation (supervised) learning of optimal examples is used as a network training paradigm. The resulting models are...
Preprint
Full-text available
Optimal control holds great potential to improve a variety of robotic applications. The application of optimal control on-board limited platforms has been severely hindered by the large computational requirements of current state of the art implementations. In this work, we make use of a deep neural network to directly map the robot states to contr...
Preprint
Full-text available
Reliable pose estimation of uncooperative satellites is a key technology for enabling future on-orbit servicing and debris removal missions. The Kelvins Satellite Pose Estimation Challenge aims at evaluating and comparing monocular vision-based approaches and pushing the state-of-the-art on this problem. This work is based on the Satellite Pose Est...
Article
Imitation learning is a control design paradigm that seeks to learn a control policy reproducing demonstrations from expert agents. By substituting expert demonstrations for optimal behaviours, the same paradigm leads to the design of control policies closely approximating the optimal state-feedback. This approach requires training a machine learni...
Article
Full-text available
In the design of multitarget interplanetary missions, there are always many options available, making it often impractical to optimize in detail each transfer trajectory in a preliminary search phase. Fast and accurate estimation methods for optimal transfers are thus of great value. In this paper, deep feed-forward neural networks are employed to...
Article
Full-text available
European Space Aqency (ESA)’s PROBA-V Earth observation (EO) satellite enables us to monitor our planet at a large scale to study the interaction between vegetation and climate, and provides guidance for important decisions on our common global future. However, the interval at which high-resolution images are recorded spans over several days, in co...
Conference Paper
Full-text available
The 10th edition of the Global Trajectory Optimization Competition (GTOC-X) invited participants all over the world to compete against each other to design efficient missions with the goal to settle our galaxy. Leveraging concepts of interstellar space travel like generational ships, the participants were tasked to develop settlement plans as each...
Article
The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of spacecraft guidance dynamics and control, giving selected examples on success stories that have been motivated...
Conference Paper
Full-text available
A number of applications to interplanetary trajectories have been recently proposed based on deep networks. These approaches often rely on the availability of a large number of optimal trajectories to learn from. In this paper we introduce a new method to quickly create millions of optimal spacecraft trajectories from a single nominal trajectory. A...
Conference Paper
While optimized neural network architectures are essential for effective training with gradient descent, their development remains a challenging and resource-intensive process full of trial-and-error iterations. We propose to encode neural networks with a differentiable variant of Cartesian Genetic Programming (dCGPANN) and present a memetic algori...
Preprint
Full-text available
The ability to design complex neural network architectures which enable effective training by stochastic gradient descent has been the key for many achievements in the field of deep learning. However, developing such architectures remains a challenging and resourceintensive process full of trial-and-error iterations. All in all, the relation betwee...
Preprint
ESA's PROBA-V Earth observation satellite enables us to monitor our planet at a large scale, studying the interaction between vegetation and climate and provides guidance for important decisions on our common global future. However, the interval at which high resolution images are recorded spans over several days, in contrast to the availability of...
Chapter
After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an inter...
Preprint
Full-text available
A number of applications to interplanetary trajectories have been recently proposed based on deep networks. These approaches often rely on the availability of a large number of optimal trajectories to learn from. In this paper we introduce a new method to quickly create millions of optimal spacecraft trajectories from a single nominal trajectory. A...
Preprint
Full-text available
Solving optimal control problems is well known to be very computationally demanding. In this paper we show how a combination of Pontryagin's minimum principle and machine learning can be used to learn optimal feedback controllers for a parametric cost function. This enables an unmanned system with limited computational resources to run optimal feed...
Preprint
Full-text available
In the design of multitarget interplanetary missions, there are always many options available, making it often impractical to optimize in detail each transfer trajectory in a preliminary search phase. Fast and accurate estimation methods for optimal transfers are thus of great value. In this paper, deep feed-forward neural networks are employed to...
Preprint
Imitation learning is a control design paradigm that seeks to learn a control policy reproducing demonstrations from experts. By substituting expert's demonstrations for optimal behaviours, the same paradigm leads to the design of control policies closely approximating the optimal state-feedback. This approach requires training a machine learning a...
Preprint
Full-text available
The rapid developments of Artificial Intelligence in the last decade are influencing Aerospace Engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of Spacecraft Guidance Dynamics and Control, giving selected examples on success stories that have been motivated...
Preprint
Research has shown how the optimal feedback control of several non linear systems of interest in aerospace applications can be represented by deep neural architectures and trained using techniques including imitation learning, reinforcement learning and evolutionary algorithms. Such deep architectures are here also referred to as Guidance and Contr...
Article
Roger Walker and colleagues consider the potential for sending nanospacecraft into deep space.
Article
Full-text available
In this paper we study space debris removal from a game-theoretic perspective. In particular we focus on the question whether and how self-interested agents can cooperate in this dilemma, which resembles a tragedy of the commons scenario. We compare centralised and decentralised solutions and the corresponding price of anarchy, which measures the e...
Article
The preliminary mission design of spacecraft missions to asteroids often involves, in the early phases, the selection of candidate target asteroids. The final result of such an analysis is a list of asteroids, ranked with respect to the necessary propellant to be used, that the spacecraft could potentially reach. In this paper we investigate the se...
Article
Full-text available
After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an inter...
Article
Full-text available
Although machine learning holds an enormous promise for autonomous space robots, it is currently not employed because of the inherent uncertain outcome of learning processes. In this article we investigate a learning mechanism, Self-Supervised Learning (SSL), which is very reliable and hence an important candidate for real-world deployment even on...
Conference Paper
During the initial phase of space trajectory planning and optimization, it is common to have to solve large dimensional global optimization problems. In particular continuous low-thrust propulsion is computationally very intensive to obtain optimal solutions. In this work, we investigate the application of machine learning regressors to estimate th...
Conference Paper
Full-text available
The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of bodies. Special focus is placed...
Conference Paper
Full-text available
We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning. The resulting machine learn...
Article
Full-text available
Jumping spiders are capable of estimating the distance to their prey relying only on the information from one of their main eyes. Recently, it has been shown that jumping spiders perform this estimation based on image defocus cues. In order to gain insight into the mechanisms involved in this blur-to-distance mapping as performed by the spider and...
Conference Paper
Full-text available
Finding the optimal ordering of k-subsets with respect to an objective function is known to be an extremely challenging problem. In this paper we introduce a new objective for this task, rooted in the problem of star identification on spacecrafts: subsets of detected spikes are to be generated in an ordering that minimizes time to detection of a va...
Data
Slides presenting the paper "Optimal orderings of k-subsets for star identification" (https://doi.org/10.1109/SSCI.2016.7850106), shown at SSCI 2016 (http://ssci2016.cs.surrey.ac.uk/). Code at: http://github.com/neXyon/k-subsets Sequences database at: http://www.esa.int/gsp/ACT/ai/projects/star_trackers.html
Chapter
Full-text available
The design of interplanetary trajectories often involves a preliminary search for options later refined/assembled into one final trajectory. It is this broad search that, often being intractable, inspires the international event called Global Trajectory Optimization Competition. In the first part of this chapter, we introduce some fundamental probl...
Conference Paper
We investigate the use of deep artificial neural networks to approximate the optimal state-feedback control of continuous time, deterministic, non-linear systems. The networks are trained in a supervised manner using trajectories generated by solving the optimal control problem via the Hermite-Simpson transcription method. We find that deep network...
Conference Paper
We consider the problem of optimally transferring a spacecraft from a starting to a target asteroid. We introduce novel approximations for important quantities characterizing the optimal transfer in case of short transfer times (asteroid hops). We propose and study in detail approximations for the phasing value φ, for the maximum initial mass m* an...
Article
Full-text available
We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning. The resulting machine learn...
Article
Full-text available
Recent research on deep learning, a set of machine learning techniques able to learn deep architectures, has shown how robotic perception and action greatly benefits from these techniques. In terms of spacecraft navigation and control system, this suggests that deep architectures may be considered now to drive all or part of the on-board decision m...
Article
Full-text available
We analyse active space debris removal efforts from a strategic, game-theoretical perspective. Space debris is non-manoeuvrable, human-made objects orbiting Earth, which pose a significant threat to operational spacecraft. Active debris removal missions have been considered and investigated by different space agencies with the goal to protect valua...