The escalating threat of space debris, with over 36,000 tracked objects in Low Earth Orbit (LEO), has made active removal a critical priority, underscored by the numerous collision avoidance maneuvers performed annually by assets like the International Space Station (ISS). This thesis addresses this challenge by proposing a mothership-based removal strategy. The mothership applies deorbiting kits to debris, allowing it to move efficiently between targets while the deorbited objects re-enter the atmosphere. This approach optimizes the mission by enabling the removal of multiple debris in a single operation. The problem is formulated as a Time-Dependent Rooted Orienteering Problem (TDROP). Given its NP-hard computational complexity it cannot be solved in exact way. To this purpose a matheuristic approach, based on a Mixed Integer Linear Programming (MILP) model, is proposed. The method employs a rolling horizon strategy, where the time horizon is discretized in time slots and the MILP is solved iteratively. In each step, the mothership's starting position is fixed based on the location of the solution computed at the previous iteration. To manage computational complexity, the debris pool is limited to 20 high-priority elements selected via environmental indices. A preprocessing step further reduces the problem size by clustering spatially close debris into single targets. The MILP model for each iteration is solved using the CPLEX software. The results confirm the effectiveness of the proposed strategy, producing feasible and efficient mission paths. The model's validity is confirmed through testing on real-world instances derived from the spacetrack.org catalog, demonstrating its applicability to practical scenarios.
La crescente minaccia dei detriti spaziali, con oltre 36.000 oggetti tracciati in orbita terrestre bassa (LEO), ha reso la loro rimozione attiva una priorità critica, sottolineata dalle numerose manovre anti-collisione eseguite annualmente da asset come la Stazione Spaziale Internazionale (ISS). Questa tesi affronta tale sfida proponendo una strategia di rimozione basata su una "mothership" (nave madre). La mothership applica dei kit di deorbiting ai detriti, spostandosi in modo efficiente tra un detrito e l'altro mentre gli oggetti deorbitati rientrano nell'atmosfera. Questo approccio ottimizza la missione, consentendo la rimozione di più detriti in una singola operazione. Il problema è formulato come un Time-Dependent Rooted Orienteering Problem (TDROP). Data la sua complessità computazionale NP-hard, il problema non può essere risolto in modo esatto. Per questo scopo viene proposto un approccio matheuristico basato su un modello di Programmazione Lineare Intera Mista (MILP). Il metodo impiega una strategia a orizzonte mobile (rolling horizon), in cui l'orizzonte temporale viene discretizzato in slot temporali e il modello MILP è risolto in modo iterativo. In ogni passo, la posizione di partenza della mothership viene fissata sulla base della locazione finale della soluzione calcolata nell'iterazione precedente. Per gestire la complessità computazionale, l'insieme dei detriti è limitato a 20 elementi ad alta priorità, selezionati tramite indici ambientali. Un'ulteriore riduzione è ottenuta tramite un pre-processing che raggruppa detriti spazialmente vicini in un unico cluster. Il modello MILP di ogni iterazione è risolto utilizzando il software CPLEX. I risultati confermano l'efficacia della strategia proposta, generando traiettorie di missione fattibili ed efficienti. La validità del modello è confermata da test su istanze reali derivate dal catalogo spacetrack.org, dimostrandone l'applicabilità a scenari pratici.
Design and optimization of a mothership-based active debris removal strategy
Menicaglia, Matteo
2024/2025
Abstract
The escalating threat of space debris, with over 36,000 tracked objects in Low Earth Orbit (LEO), has made active removal a critical priority, underscored by the numerous collision avoidance maneuvers performed annually by assets like the International Space Station (ISS). This thesis addresses this challenge by proposing a mothership-based removal strategy. The mothership applies deorbiting kits to debris, allowing it to move efficiently between targets while the deorbited objects re-enter the atmosphere. This approach optimizes the mission by enabling the removal of multiple debris in a single operation. The problem is formulated as a Time-Dependent Rooted Orienteering Problem (TDROP). Given its NP-hard computational complexity it cannot be solved in exact way. To this purpose a matheuristic approach, based on a Mixed Integer Linear Programming (MILP) model, is proposed. The method employs a rolling horizon strategy, where the time horizon is discretized in time slots and the MILP is solved iteratively. In each step, the mothership's starting position is fixed based on the location of the solution computed at the previous iteration. To manage computational complexity, the debris pool is limited to 20 high-priority elements selected via environmental indices. A preprocessing step further reduces the problem size by clustering spatially close debris into single targets. The MILP model for each iteration is solved using the CPLEX software. The results confirm the effectiveness of the proposed strategy, producing feasible and efficient mission paths. The model's validity is confirmed through testing on real-world instances derived from the spacetrack.org catalog, demonstrating its applicability to practical scenarios.File | Dimensione | Formato | |
---|---|---|---|
2025_07_Menicaglia_Tesi_01.pdf
non accessibile
Dimensione
9.82 MB
Formato
Adobe PDF
|
9.82 MB | Adobe PDF | Visualizza/Apri |
2025_07_Menicaglia_Executive_Summary_02.pdf
accessibile in internet per tutti
Dimensione
476.88 kB
Formato
Adobe PDF
|
476.88 kB | Adobe PDF | Visualizza/Apri |
I documenti in POLITesi sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/10589/240979