Multi-agent collaborative search : an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher

Multi agent collaborative search based on Tchebycheff decomposition

This paper presents a novel formulation of Multi Agent Collaborative Search, for multi-objective optimization, based on Tchebycheff decomposition. A population of agents combines heuristics that aim at exploring the search space both globally (social moves) and in a neighborhood of each agent (individualistic moves). In this novel formulation the selection process is based on a combination of Tchebycheff scalarization and Pareto dominance. Furthermore, while in the previous implementation, social actions were applied to the whole population of agents and individualistic actions only to an elite sub-population, in this novel formulation this mechanism is inverted. The novel agent-based algorithm is tested at first on a standard benchmark of difficult problems and then on two specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi objective optimization algorithms. The results demonstrate that this novel agent-based search has better performance with respect to its predecessor in a number of cases and converges better than the other state-of-the-art algorithms with a better spreading of the solutions

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

Manoeuvre Planning Architecture for the Optimisation of Spacecraft Formation Flying Reconfiguration Manoeuvres

Formation flying of multiple spacecraft collaborating toward the same goal is fast becoming a reality for space mission designers. Often the missions require the spacecraft to perform translational manoeuvres relative to each other to achieve some mission objective. These manoeuvres need to be planned to ensure the safety of the spacecraft in the formation and to optimise fuel management throughout the fleet. In addition to these requirements is it desirable for this manoeuvre planning to occur autonomously within the fleet to reduce operations cost and provide greater planning flexibility for the mission. One such mission that would benefit from this type of manoeuvre planning is the European Space Agency’s DARWIN mission, designed to search for extra-solar Earth-like planets using separated spacecraft interferometry. This thesis presents a Manoeuvre Planning Architecture for the DARWIN mission. The design of the Architecture involves identifying and conceptualising all factors affecting the execution of formation flying manoeuvres at the Sun/Earth libration point L2. A systematic trade-off analysis of these factors is performed and results in a modularised Manoeuvre Planning Architecture for the optimisation of formation flying reconfiguration manoeuvres. The Architecture provides a means for DARWIN to autonomously plan manoeuvres during the reconfiguration mode of the mission. The Architecture consists of a Science Operations Module, a Position Assignment Module, a Trajectory Design Module and a Station-keeping Module that represents a multiple multi-variable optimisation approach to the formation flying manoeuvre planning problem. The manoeuvres are planned to incorporate target selection for maximum science returns, collision avoidance, thruster plume avoidance, manoeuvre duration minimisation and manoeuvre fuel management (including fuel consumption minimisation and formation fuel balancing). With many customisable variables the Architecture can be tuned to give the best performance throughout the mission duration. The implementation of the Architecture highlights the importance of planning formation flying reconfiguration manoeuvres. When compared with a benchmark manoeuvre planning strategy the Architecture demonstrates a performance increase of 27% for manoeuvre scheduling and fuel savings of 40% over a fifty target observation tour. The Architecture designed in this thesis contributes to the field of spacecraft formation flying analysis on various levels. First, the manoeuvre planning is designed at the mission level with considerations for mission operations and station-keeping included in the design. Secondly, the requirements analysis and implementation of Science Operation Module represent a unique insight into the complexity of observation scheduling for exo-planet analysis missions and presents a robust method for autonomously optimising that scheduling. Thirdly, in-depth analyses are performed on DARWIN-based modifications of existing manoeuvre optimisation strategies identifying their strengths and weaknesses and ways to improve them. Finally, though not implemented in this thesis, the design of a Station-keeping Module is provided to add station-keeping optimisation functionality to the Architecture

Preliminary design of debris removal missions by means of simplified models for low-thrust, many-revolution transfers

This paper presents a novel approach for the preliminary design of Low-Thrust (LT), many-revolution transfers. The main feature of the novel approach is a considerable reduction in the control parameters and a consequent gain in computational speed. Each spiral is built by using a predefined pattern for thrust direction and switching structure. The pattern is then optimised to minimise propellant consumption and transfer time. The variation of the orbital elements due to the propulsive thrust is computed analytically from a first-order solution of the perturbed Keplerian motion. The proposed approach allows for a realistic estimation of ΔV cost and time of flight required to transfer a spacecraft between two arbitrary orbits. Eccentricity and plane changes are both taken into account. The novel approach is applied here to the design of missions for the removal of space debris by means of an Ion Beam Shepherd (IBS) Spacecraft. In particular, two slightly different variants of the proposed low-thrust control model are used for the two main phases of the debris removal mission, i.e. the rendezvous with the target object and its removal. Thanks to their relatively low computational cost they can be included in a multiobjective optimisation problem in which the sequence and timing of the removal of five hypothetical pieces of debris are optimised in order to minimise both propellant consumption and mission duration

Improved archiving and search strategies for multi agent collaborative search

This paper presents a new archiving strategy and some modified search heuristics for the Multi Agent Collaborative Search algorithm (MACS). MACS is a memetic scheme for multi-objective optimisation that combines the local exploration of the neighbourhood of some virtual agents with social actions to advance towards the Pareto front. The new archiving strategy is based on the physical concept of minimising the potential energy of a cloud of points each of which repels the others. Social actions have been modified to better exploit the information in the archive and local actions dynamically adapt the maximum number of coordinates explored in the pattern search heuristic. The impact of these modifications is tested on a standard benchmark and the results are compared against MOEA/D and a previous version of MACS. Finally, a real space related problem is tackled

Global optimisation of multiple gravity assist trajectories

Multiple gravity assist (MGA) trajectories represent a particular class of space trajectories in which a spacecraft exploits the encounter with one or more celestial bodies to change its velocity vector; they have been essential to reach high Delta-v targets with low propellant consumption. The search for optimal transfer trajectories can be formulated as a mixed combinatorial-continuous global optimisation problem; however, it is known that the problem is difficult to solve, especially if deep space manoeuvres (DSM) are considered. This thesis addresses the automatic design of MGA trajectories through global search techniques, in answer to the requirements of having a large number of mission options in a short time, during the preliminary design phase. Two different approaches are presented. The first is a two-level approach: a number of feasible planetary sequences are initially generated; then, for each one, families of the MGA trajectories are built incrementally. The whole transfer is decomposed into sub-problems of smaller dimension and complexity, and the trajectory is progressively composed by solving one problem after the other. At each incremental step, a stochastic search identifies sets of feasible solutions: this region is preserved, while the rest of the search space is pruned out. The process iterates by adding one planet-to-planet leg at a time and pruning the unfeasible portion of the solution space. Therefore, when another leg is added to the trajectory, only the feasible set for the previous leg is considered and the search space is reduced. It is shown, through comparative tests, how the proposed incremental search performs an effective pruning of the search space, providing families of optimal solutions with a lower computational cost than a non-incremental approach. Known deterministic and stochastic methods are used for the comparison. The algorithm is applied to real MGA case studies, including the ESA missions BepiColombo and Laplace. The second approach performs an integrated search for the planetary sequence and the associated trajectories. The complete design of an MGA trajectory is formulated as an autonomous planning and scheduling problem. The resulting scheduled plan provides the planetary sequence for a MGA trajectory and a good estimation of the optimality of the associated trajectories. For each departure date, a full tree of possible transfers from departure to destination is generated. An algorithm inspired by Ant Colony Optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination, adding one node at a time, using a probability function to select one of the feasible directions. Unlike standard ACO, a taboo-based heuristics prevents ants from re-exploring the same solutions. This approach is applied to the design of optimal transfers to Saturn (inspired by Cassini) and to Mercury, and it demonstrated to be very competitive against known traditional stochastic population-based techniques

Manoeuvre planning architecture for the optimisation of spacecraft formation flying reconfiguration manoeuvres

Formation flying of multiple spacecraft collaborating toward the same goal is fast becoming a reality for space mission designers. Often the missions require the spacecraft to perform translational manoeuvres relative to each other to achieve some mission objective. These manoeuvres need to be planned to ensure the safety of the spacecraft in the formation and to optimise fuel management throughout the fleet. In addition to these requirements is it desirable for this manoeuvre planning to occur autonomously within the fleet to reduce operations cost and provide greater planning flexibility for the mission. One such mission that would benefit from this type of manoeuvre planning is the European Space Agency’s DARWIN mission, designed to search for extra-solar Earth-like planets using separated spacecraft interferometry. This thesis presents a Manoeuvre Planning Architecture for the DARWIN mission. The design of the Architecture involves identifying and conceptualising all factors affecting the execution of formation flying manoeuvres at the Sun/Earth libration point L2. A systematic trade-off analysis of these factors is performed and results in a modularised Manoeuvre Planning Architecture for the optimisation of formation flying reconfiguration manoeuvres. The Architecture provides a means for DARWIN to autonomously plan manoeuvres during the reconfiguration mode of the mission. The Architecture consists of a Science Operations Module, a Position Assignment Module, a Trajectory Design Module and a Station-keeping Module that represents a multiple multi-variable optimisation approach to the formation flying manoeuvre planning problem. The manoeuvres are planned to incorporate target selection for maximum science returns, collision avoidance, thruster plume avoidance, manoeuvre duration minimisation and manoeuvre fuel management (including fuel consumption minimisation and formation fuel balancing). With many customisable variables the Architecture can be tuned to give the best performance throughout the mission duration. The implementation of the Architecture highlights the importance of planning formation flying reconfiguration manoeuvres. When compared with a benchmark manoeuvre planning strategy the Architecture demonstrates a performance increase of 27% for manoeuvre scheduling and fuel savings of 40% over a fifty target observation tour. The Architecture designed in this thesis contributes to the field of spacecraft formation flying analysis on various levels. First, the manoeuvre planning is designed at the mission level with considerations for mission operations and station-keeping included in the design. Secondly, the requirements analysis and implementation of Science Operation Module represent a unique insight into the complexity of observation scheduling for exo-planet analysis missions and presents a robust method for autonomously optimising that scheduling. Thirdly, in-depth analyses are performed on DARWIN-based modifications of existing manoeuvre optimisation strategies identifying their strengths and weaknesses and ways to improve them. Finally, though not implemented in this thesis, the design of a Station-keeping Module is provided to add station-keeping optimisation functionality to the Architecture.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Preliminary Design of Debris Removal Missions by Means of Simplified Models for Low-Thrust, Many-Revolution Transfers

This paper presents a novel approach for the preliminary design of Low-Thrust, many-revolution transfers. The main feature of the novel approach is a considerable reduction in the control parameters and a consequent gain in computational speed. Each spiral is built by using a predefined pattern for thrust direction and switching structure. The pattern is then optimised to minimise propellant consumption and transfer time. The variation of the orbital elements due to the thrust is computed analytically from a first-order solution of the perturbed Keplerian motion. The proposed approach allows for a realistic estimation of {\Delta}V and time of flight required to transfer a spacecraft between two arbitrary orbits. Eccentricity and plane changes are both accounted for. The novel approach is applied here to the design of missions for the removal of space debris by means of an Ion Beam Shepherd Spacecraft. In particular, two slightly different variants of the proposed low-thrust control model are used for the different phases of the mission. Thanks to their low computational cost they can be included in a multiobjective optimisation problem in which the sequence and timing of the removal of five pieces of debris are optimised to minimise propellant consumption and mission duration

Preliminary Design of Debris Removal Missions by Means of Simplified Models for Low-Thrust, Many-Revolution Transfers

This paper presents a novel approach for the preliminary design of Low-Thrust, many-revolution transfers. The main feature of the novel approach is a considerable reduction in the control parameters and a consequent gain in computational speed. Each spiral is built by using a predefined pattern for thrust direction and switching structure. The pattern is then optimised to minimise propellant consumption and transfer time. The variation of the orbital elements due to the thrust is computed analytically from a first-order solution of the perturbed Keplerian motion. The proposed approach allows for a realistic estimation of {\Delta}V and time of flight required to transfer a spacecraft between two arbitrary orbits. Eccentricity and plane changes are both accounted for. The novel approach is applied here to the design of missions for the removal of space debris by means of an Ion Beam Shepherd Spacecraft. In particular, two slightly different variants of the proposed low-thrust control model are used for the different phases of the mission. Thanks to their low computational cost they can be included in a multiobjective optimisation problem in which the sequence and timing of the removal of five pieces of debris are optimised to minimise propellant consumption and mission duration