Global Trajectory Optimisation : Can We Prune the Solution Space When Considering Deep Space Manoeuvres? [Final Report]

This document contains a report on the work done under the ESA/Ariadna study 06/4101 on the global optimization of space trajectories with multiple gravity assist (GA) and deep space manoeuvres (DSM). The study was performed by a joint team of scientists from the University of Reading and the University of Glasgow

Planetary protection efficiency by a small kinetic impactor

This paper re-examines the deflection concept with, arguably, the highest technological readiness level: the kinetic impactor. A baseline design for the concept with a 1,000 kg spacecraft launched from Earth is defined. The paper then analyses the capability of the kinetic spacecraft to offer planetary protection, thus, deflecting asteroids on a collision trajectory with Earth. In order to give a realistic estimate, the paper uses a set of more than 17 thousand Earth-impacting trajectories and has computed the largest asteroid mass that could be deflected to a sufficiently safe distance from Earth. By using the relative impact frequency of the different impact orbits, which can be estimated by modeling the asteroid population and the collision probability of the different impact geometries, a figure on the level of planetary protection that such a system could offer can be estimated. The results show that such a system could offer very high levels of protection, around 97% deflection reliability, against objects between 15 to 75 meters, while decreases for larger sizes

Impact hazard protection efficiency by a small kinetic impactor

In this paper the ability of a small kinetic impactor spacecraft to mitigate an Earth-threatening asteroid is assessed by means of a novel measure of efficiency. This measure estimates the probability of a space system to deflect a single randomly-generated Earth-impacting object to a safe distance from the Earth. This represents a measure of efficiency that is not biased by the orbital parameters of a test-case object. A vast number of virtual Earth-impacting scenarios are investigated by homogenously distributing in orbital space a grid of 17,518 Earth impacting trajectories. The relative frequency of each trajectory is estimated by means Opik’s theory and Bottke’s near Earth objects model. A design of the entire mitigation mission is performed and the largest deflected asteroid computed for each impacting trajectory. The minimum detectable asteroid can also be estimated by an asteroid survey model. The results show that current technology would likely suffice against discovered airburst and local damage threats, whereas larger space systems would be necessary to reliably tackle impact hazard from larger threats. For example, it is shown that only 1,000 kg kinetic impactor would suffice to mitigate the impact threat of 27.1% of objects posing similar threat than that posed by Apophis

A Parallel Processing and Diversified-Hidden-Gene-based Genetic Algorithm Framework for Fuel-Optimal Trajectory Design for Interplanetary Spacecraft Missions

This thesis proposes a new parallel computing genetic algorithm framework for designing fuel-optimal trajectories for interplanetary spacecraft missions. The framework can capture the deep search space of the problem with the use of a fixed chromosome structure and hidden-genes concept, can explore the diverse set of candidate solutions with the use of the adaptive and twin-space crowding techniques and, can execute on any high-performance computing (HPC) platform with the adoption of the portable message passing interface (MPI) standard. The algorithm is implemented in C++ with the use of the MPICH implementation of the MPI standard. The algorithm uses a patched-conic approach with two-body dynamics assumptions. New procedures are developed for determining trajectories in the V-infinity-leveraging legs of the flight from the launch and non-launch planets and, deep-space maneuver legs of the flight from the launch and non-launch planets. The chromosome structure maintains the time of flight as a free parameter within certain boundaries. The fitness or the cost function of the algorithm uses only the mission $\Delta V$, and does not include time of flight. The optimization is conducted with two variations for the minimum mission gravity-assist sequence, the 4-gravity-assist, and the 3-gravity-assist, with a maximum of 5 gravity-assists allowed in both the cases. The optimal trajectories discovered using the framework in both of the cases demonstrate the success of this framework

Using genetic algorithm optimization as a multi-gravity assist trajectory design tool

This master thesis presents the development of a genetic algorithm for optimizing interplanetary trajectories using multi-planetary gravity assists while considering time as an additional objective in the fitness evaluation. The objective of the research is to address the challenges of designing efficient trajectories that minimize both delta-v and travel duration. The aim of this thesis is to develop an all-encompassing deep space mission trajectory design tool where a trade-off between the total delta-v used and the arrival time can be made, in the interest of the overall mission profile. The results obtained highlight the effectiveness of genetic algorithms in finding optimal multi-planetary gravity assist trajectories and contribute to the advancement of trajectory optimization techniques for future space missions

Inverse Reinforcement Learning in Large State Spaces via Function Approximation

This paper introduces a new method for inverse reinforcement learning in large-scale and high-dimensional state spaces. To avoid solving the computationally expensive reinforcement learning problems in reward learning, we propose a function approximation method to ensure that the Bellman Optimality Equation always holds, and then estimate a function to maximize the likelihood of the observed motion. The time complexity of the proposed method is linearly proportional to the cardinality of the action set, thus it can handle large state spaces efficiently. We test the proposed method in a simulated environment, and show that it is more accurate than existing methods and significantly better in scalability. We also show that the proposed method can extend many existing methods to high-dimensional state spaces. We then apply the method to evaluating the effect of rehabilitative stimulations on patients with spinal cord injuries based on the observed patient motions.Comment: Experiment update

CAMELOT - computational-analytical multi-fidelity low-thrust optimisation toolbox

CAMELOT (Computational-Analytical Multi-fidelity Low-thrust Optimisation Toolbox) is a toolbox for the fast preliminary design and optimisation of low-thrust trajectories. It solves highly complex combinatorial problems to plan multi-target missions characterised by long spirals including different perturbations. In order to do so, CAMELOT implements a novel multi-fidelity approach combining analytical surrogate modelling and accurate computational estimations of the mission cost. Decisions are then made by using two pptimisation engines included in the toolbox, a single objective global optimiser and a combinatorial optimisation algorithm. CAMELOT has been applied to a variety of applications: from the design of interplanetary trajectories to the optimal deorbiting of space debris, from the deployment of constellations to on-orbit servicing. In this paper the main elements of CAMELOT are described and two space mission design problems solved using the toolbox are described

Automated Sensitivity Analysis of Interplanetary Trajectories for Optimal Mission Design

This work describes a suite of Python tools known as the Python EMTG Automated Trade Study Application (PEATSA). PEATSA was written to automate the operation of trajectory optimization software, simplify the process of performing sensitivity analysis, and was ultimately found to out-perform a human trajectory designer in unexpected ways. These benefits will be discussed and demonstrated on sample mission designs