Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

Editoria

Spacecraft Trajectory Optimization: A review of Models, Objectives, Approaches and Solutions

This article is a survey paper on solving spacecraft trajectory optimization problems. The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, development of an approach and obtaining the solution of the problem. Several subcategories for each step have been identified and described. Subsequently, important classifications and their characteristics have been discussed for solving the problems. Finally, a discussion on how to choose an element of each step for a given problem is provided.La Caixa, TIN2016-78365-

Multi-objective trajectory optimization of Space Maneuver Vehicle using adaptive differential evolution and modified game theory

Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem

A Hybrid Technique applied to the Intermediate-Target Optimal Control Problem

The DoD has introduced the concept of Manned-Unmanned Teaming, a subset of which is the loyal wingman. Optimal control techniques have been proposed as a method for rapidly solving the intermediate-target (mid-point constraint) optimal control problem. Initial results using direct orthogonal collocation and a gradient-based method for solving the resulting nonlinear program reveals a tendency to converge to or to get `stuck’ in locally optimal solutions. The literature suggested a hybrid technique in which a particle swarm optimization is used to quickly find a neighborhood of a more globally minimal solution, at which point the algorithm switches to a gradient-based nonlinear programming solver to converge on the globally optimal solution. The work herein applies the hybrid optimization technique to rapidly solve the loyal wingman optimal control problem. After establishing the background and describing the loyal wingman particle swarm optimization algorithm, the problem is solved first using the gradient-based direct orthogonal collocation method, then re-solved using a hybrid approach in which the results of the particle swarm optimization algorithm are used as the initial guess for the gradient-based direct orthogonal collocation method. Results comparing the final trajectory and convergence time, demonstrate the hybrid technique as a reliable method for producing rapid, autonomous, and feasible solutions to the loyal wingman optimal control problem

High-fidelity trajectory optimization for aeroassisted vehicles using variable order pseudospectral method

In this study, the problem of time-optimal reconnaissance trajectory design for the aeroassisted vehicle is considered. Different from most works reported previously, we explore the feasibility of applying a high-order aeroassisted vehicle dynamic model to plan the optimal flight trajectory such that the gap between the simulated model and the real system can be narrowed. A highly-constrained optimal control model containing six-degree-of-freedom vehicle dynamics is established. To solve the formulated high-order trajectory planning model, a pipelined optimization strategy is illustrated. This approach is based on the variable order Radau pseudospectral method, indicating that the mesh grid used for discretizing the continuous system experiences several adaption iterations. Utilization of such a strategy can potentially smooth the flight trajectory and improve the algorithm convergence ability. Numerical simulations are reported to demonstrate some key features of the optimized flight trajectory. A number of comparative studies are also provided to verify the effectiveness of the applied method as well as the high-order trajectory planning model

Avionics-based GNSS integrity augmentation for unmanned aerial systems sense-and-avoid

This paper investigates the synergies between a GNSS Avionics Based Integrity Augmentation (ABIA) system and a novel Unmanned Aerial System (UAS) Sense-and-Avoid (SAA) architecture for cooperative and non-cooperative scenarios. The integration of ABIA with SAA has the potential to provide an integrity-augmented SAA solution that will allow the safe and unrestricted access of UAS to commercial airspace. The candidate SAA system uses Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) for the cooperative case. In the non-cooperative scenario, the system employs navigation-based image stabilization with image morphology operations and a multi-branch Viterbi filter for obstacle detection, which allows heading estimation. The system utilizes a Track-to-Track (T3) algorithm for data fusion that allows combining data from different tracks obtained with FLS and/or ADS-B depending on the scenario. Successively, it utilizes an Interacting Multiple Model (IMM) algorithm to estimate the state vector allowing a prediction of the intruder trajectory over a specified time horizon. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuver is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole SAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Various simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented SAA (IAS) architecture. The selected host platform was the AEROSONDE Unmanned Aerial Vehicle (UAV) and the simulation cases addressed a variety of cooperative and non-cooperative scenarios in a representative cross-section of the AEROSONDE operational flight envelope. The simulation results show that the proposed IAS architecture is an excellent candidate to perform high-integrity Collision Detection and Resolution (CD&R) utilizing GNSS as the primary source of navigation data, providing solid foundation for future research and developments in this domain

Avionics-based GNSS integrity augmentation for unmanned aerial systems sense-and-avoid

This paper investigates the synergies between a GNSS Avionics Based Integrity Augmentation (ABIA) system and a novel Unmanned Aerial System (UAS) Sense-and-Avoid (SAA) architecture for cooperative and non-cooperative scenarios. The integration of ABIA with SAA has the potential to provide an integrity-augmented SAA solution that will allow the safe and unrestricted access of UAS to commercial airspace. The candidate SAA system uses Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) for the cooperative case. In the non-cooperative scenario, the system employs navigation-based image stabilization with image morphology operations and a multi-branch Viterbi filter for obstacle detection, which allows heading estimation. The system utilizes a Track-to-Track (T3) algorithm for data fusion that allows combining data from different tracks obtained with FLS and/or ADS-B depending on the scenario. Successively, it utilizes an Interacting Multiple Model (IMM) algorithm to estimate the state vector allowing a prediction of the intruder trajectory over a specified time horizon. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuver is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole SAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Various simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented SAA (IAS) architecture. The selected host platform was the AEROSONDE Unmanned Aerial Vehicle (UAV) and the simulation cases addressed a variety of cooperative and non-cooperative scenarios in a representative cross-section of the AEROSONDE operational flight envelope. The simulation results show that the proposed IAS architecture is an excellent candidate to perform high-integrity Collision Detection and Resolution (CD&R) utilizing GNSS as the primary source of navigation data, providing solid foundation for future research and developments in this domain

Optimal Finite Thrust Guidance Methods for Constrained Satellite Proximity Operations Inspection Maneuvers

Algorithms are developed to find optimal guidance for an inspector satellite operating nearby a resident space object (RSO). For a non-maneuvering RSO, methods are first developed for a satellite subject to maximum slew rates to conduct an initial inspection of an RSO, where the control variables include the throttle level and direction of the thrust. Second, methods are developed to optimally maneuver a satellite with on/off thrusters into a natural motion circumnavigation or teardrop trajectory, subject to lighting and collision constraints. It is shown that for on/off thrusters, a control sequence can be parameterized to a relatively small amount of control variables and the relative states can be analytically propagated as a function of those control variables. For a maneuvering RSO, differential games are formulated and solved for an inspector satellite to achieve multiple inspection goals, such as aligning with the Sun vector or matching the RSO\u27s energy. The developed algorithms lead to fuel and time savings which can increase the mission life and capabilities of inspector satellites and thus improve space situational awareness for the U.S. Air Force

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented

New Development on Sense and Avoid Strategies for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) can carry out more complex civilian and military applications with less cost and more flexibility in comparison of manned aircraft. Mid-air collision thus becomes profoundly important considering the safe operation of air transportation systems, when UAVs are increasingly used more with various applications and share the same airspace with manned air vehicles. To ensure safe flights, UAVs have to configure Sense and Avoid (S&A) systems performing necessary maneuvers to avoid collisions. After analyzing the manner of S&A system, avoidance strategies based on a subset of possible collision scenarios are proposed in this thesis. 1) To avoid a face-to-face intruder, a feasible trajectory is generated by differential geometric guidance, where the constraints of UAV dynamics are considered. 2) The Biogeography Based Optimization (BBO) approach is exploited to generate an optimal trajectory to avoid multiple intruders’ threats in the landing phase. 3) By formulating the collision avoidance problem within a Markov Decision Process (MDP) framework, a desired trajectory is produced to avoid multiple intruders in the 2D plane. 4) MDP optimization method is extended to address the problem of optimal 3D conflict resolution involving multiple aircraft. 5) Considering that the safety of UAVs is directly related to the dynamic constraints, the differential flatness technique is developed to smoothen the optimal trajectory. 6) Energy based controller is designed such that the UAV is capable of following the generated trajectory