Hardware-in-the-loop Simulations of Remote Sensing Disaster Monitoring Systems with Real-Time On-Board Computation

Spiller, Dario; Carbone, Andrea; Latorre, Francesco; Curti, Fabio

doi:10.1109/metroxraine54828.2022.9967688

Cited by 1 publication

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, the spacecraft is situated at an altitude of 3 km. This initial configuration is based on a previously published works from 2022 [36,40], ensuring a consistent and valid reference point for our analysis. The QPU results are shown in Figure 3, in terms of position, velocity, and acceleration.…”

Section: Lunar Landing Resultsmentioning

confidence: 99%

Cutting-Edge Trajectory Optimization through Quantum Annealing

Carbone,

De Grossi,

Spiller

2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

This paper introduces an innovative approach to explore the capabilities of Quantum Annealing (QA) for trajectory optimization in dynamic systems. The proposed method involves transforming trajectory optimization problems into equivalent binary optimization problems using Quadratic Unconstrained Binary Optimization (QUBO) representation. The procedure is general and adaptable, making it applicable to a wide range of optimal control problems that entail the satisfaction of dynamic, boundary, and path constraints. Specifically, the trajectory is discretized and approximated using polynomials. In contrast to the conventional approach of determining the polynomial degree (n) solely based on the number of boundary conditions, a specific factor is introduced in our method to augment the polynomial degree. As a result, the ultimate polynomial degree is calculated as a composite of two components: n = l + (m−1), where m denotes the count of boundary conditions and l signifies the number of independent variables. By leveraging inverse dynamics, the control required to follow the approximated trajectory can be determined as a linear function of independent variables l. As a result, the optimization function, which is represented by the integral of the square of the control, can be formulated as a QUBO problem and the QA is employed to find the optimal binary solutions.

show abstract

Section: Lunar Landing Resultsmentioning

confidence: 99%