Evolutionary Optimization for Active Debris Removal Mission Planning

Active debris removal missions require an accurate planning for maximizing mission payout, by reaching the maximum number of potential orbiting targets in a given region of space. Such a problem is known to be computationally demanding and the present paper provides a technique for preliminary mission planning based on a novel evolutionary optimization algorithm, which identifies the best sequence of debris to be captured and/or deorbited. A permutation-based encoding is introduced, which may handle multiple spacecraft trajectories. An original archipelago structure is also adopted for improving algorithm capabilities to explore the search space. As a further contribution, several crossover and mutation operators and migration schemes are tested in order to identify the best set of algorithm parameters for the considered class of optimization problems. The algorithm is numerically tested for a fictitious cloud of debris in the neighborhood of Sun-synchronous orbits, including cases with multiple chasers

Automatic trajectory planning for low-thrust active removal mission in Low-Earth Orbit

In this paper two strategies are proposed to de-orbit up to 10 non-cooperative objects per year from the region within 800 and 1400 km altitude in Low Earth Orbit (LEO). The underlying idea is to use a single servicing spacecraft to de-orbit several objects applying two different approaches. The first strategy is analogous to the Traveling Salesman Problem: the servicing spacecraft rendezvous with multiple objects in order to physically attach a de-orbiting kit that reduces the perigee of the orbit. The second strategy is analogous to the Vehicle Routing Problem: the servicing spacecraft rendezvous and docks with an object, spirals it down to a lower altitude orbit, undocks, and then spirals up to the next target. In order to maximise the number of de-orbited objects with minimum propellant consumption, an optimal sequence of targets is identified using a bio-inspired incremental automatic planning and scheduling discrete optimisation algorithm. The optimisation of the resulting sequence is realised using a direct transcription method based on an asymptotic analytical solution of the perturbed Keplerian motion. The analytical model takes into account the perturbations deriving from the $J_2$ gravitational effect and the atmospheric drag

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

Automatic planning and scheduling of active removal of non-operational satellites in low earth orbit

In this paper two novel strategies to automatically design an optimized mission to de-orbit up to 10 non-cooperative objects per year are proposed, targeting the region within 800 and 1400 km altitude in LEO. The underlying idea is to use a single servicing spacecraft to de-orbit several objects applying two different approaches.The first strategy is analogous to the Traveling Salesman Problem: The servicing spacecraft rendezvous with multiple objects in order to physically attach a de-orbiting kit that performs the re-entry. The second strategy is analogous to the Vehicle Routing Problem: The servicing spacecraft rendezvous with an object, spiral it down to a lower altitude orbit, and spiral up to the next target. In order to maximize the number of de-orbited non-operative objects with minimum propellant consumption, an optimal sequence of targets is identified using a bio-inspired incremental automatic planning and scheduling discrete optimization algorithm. The incremental planning and scheduling algorithm uses a model based on optimal low-Thrust transfer between objects. The optimization of the transfers is realized using a direct method and an analytical propagator based on a first-order solution of the perturbed Keplerian motion. The analytical model takes into account the perturbations deriving from the J2 gravitational effect and the atmospheric drag

Artificial neural networks for multi-target low-thrust missions

Multi-target missions are an attractive solution to visit multiple bodies in a single mission, increasing the scientific return and reducing the cost, compared to multiple missions to individual targets. Designing multi-target missions represents a challenging task since it requires multiple options to be estimated, given the large number of objects which can be considered as potential targets. Low-thrust propulsion systems are preferred to rendezvous multiple targets in a mission as they allow to utilise less propellant mass than high-thrust systems to perform the same trajectory. However, low-thrust trajectories are computationally expensive to compute. This PhD thesis proposes to use artificial neural networks (ANN), as a fast and accurate estimation method for optimal low-thrust transfers. An artificial neural network and a sequence search (SS) algorithm can be designed to find solutions to three kinds of multi-target global optimisation problems: (i) multiple active debris removal missions (MADR), (ii) multiple near-Earth asteroid rendezvous (MNR) missions, with the option of returning a sample to Earth, and (iii) multi-objective optimisation of low-thrust propulsion systems for multi-target missions. MADR missions allows for the disposal of inactive satellites and larger objects, preventing the build-up of space junk and allowing to replace ageing agents in a constellation. Similarly, MNR missions allow to reduce the cost of each NEA observation and increase the possibility of visiting multiple asteroids of interest in a single mission. The trained ANN is employed within a SS algorithm, based on a tree-search method and breadth-first criterion, to identify multiple rendezvous sequences and select those with lowest time of flight and/or required propellant mass. To compute the full trajectory and control history, the sequences are subsequently recalculated by using an optimal control solver based on a pseudospectral method. Also, to optimise the propulsion system for a given mission, a multi-objective optimisation using a genetic algorithm is performed, where ANNs are employed to quickly estimate the cost and duration of multi-target transfers. The results show that neural networks can estimate the duration and cost of low-thrust transfers with high accuracy, for all the three applications. Employing machine learning within a sequence search algorithm to preliminary design multitarget missions allows to significantly reduce the computational time required with respect to other most commonly used methods in the literature, while maintaining a high accuracy. Given the combinatorial nature of the problem, the benefits in terms of computational time introduced by the ANN increase exponentially with a linear increase of the number of bodies in the database

Large space structures and systems in the space station era: A bibliography with indexes (supplement 05)

Bibliographies and abstracts are listed for 1363 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1991 and July 31, 1992. Topics covered include technology development and mission design according to system, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion and solar power satellite systems