Optimal Maneuver Research Articles

Fully Safe Rendezvous Strategy in Cis-Lunar Space: Passive and Active Collision Avoidance

The future assembly of the Lunar Orbital Platform Gateway, on a near 9:2 resonant Rectilinear Halo Orbit, requires the necessity of designing safe and reliable strategies to perform rendezvous and docking with the station. The paper describes and tests a strategy to guarantee the safety of an entire rendezvous manoeuvre in the proximity of the Moon L2 Lagrangian point with respect to a specific set of failures. The safety in the far range is guaranteed through the automatic allocation of selected hold-points, whereas during close range rendezvous the safety is actively guaranteed in the presence of selected failures. The main goal of the paper is to contribute to the very limited literature about autonomous design of the guidance for rendezvous in presence of a non negligible third body influence, where safety considerations are paramount. The proposed approach is based on the exploitation of the manifold theory used in the circular restricted three body problem model to guarantee the passive safety in the far-range section, and on an optimal and reliable active collision avoidance manoeuvre to guarantee the safety for the close range approach.

Optimal Maneuvering for Autonomous Vehicle Self-Localization.

We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an l-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.

The Development of Air Escort Tactics for High-Value Airborne Assets Using Manned-Unmanned Teaming and the Study on Effective Force Disposition Using M&S

As the role of high-value air assets(e.g., AWACS, JSTARS, Rivet Joint, E-2) becomes more critical in modern warfare, the air escort for these assets blocking attacks from any potential enemy fighter also becomes vital. Without the escort, the operations of the assets become restricted. However, such an escort is not always possible due to the limited flight time of the escort fighters. In this paper, we introduce an escort tactics for high-value air assets performed by the manned-unmanned teaming composed of a transport aircraft and UAVs(unmanned aerial vehicles). In this tactics, the transport aircraft plays the role of an aircraft carrier, which carries, launches, and retrieves the UAVs. The missions of UAVs in this tactics are to detect and engage enemy fighters. We also introduce the simulation result of this tactics to identify the UAVs' required capabilities and optimal maneuvering.

Orbital maneuver technology of temporary networked satellites for earth exploration

It is very difficult to achieve effective coverage of hot-spot target in a short time period by earth perturbation for orbital maneuver, because the coverage gap of the temporary multi-satellites network in certain period of time is too large. In this paper, it is assumed that wave beam type of the satellite-payload sensor is a simple conic, the beam center of the satellite payload under the maximum lateral pendulum is calculated based on the precise ephemeris, and according to the projection ellipse of the payload wave beam on the ground, the coverage ability of the satellite payload on the ground target direction is obtained. Then the satellite is designed to encounter the ground target in the nodal period between the satellite passing the ground target and the latitude circle. The orbital maneuver control variables are calculated from the change of nodal period, and the optimal orbital maneuver control variables are selected from a specified time period. At last, the orbit maneuver in covering the target in a specified time period is achieved and the maximum revisit interval is greatly shortened.

Spatio-temporal heuristic method: a trajectory planning for automatic parking considering obstacle behavior

Purpose The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking. Design/methodology/approach To deal with dynamic obstacles for autonomous vehicles during parking, a long- and short-term mixed trajectory planning algorithm is proposed in this paper. In long term, considering obstacle behavior, A-star algorithm was improved by RS curve and potential function via spatio-temporal map to obtain a safe and efficient initial trajectory. In short term, this paper proposes a nonlinear model predictive control trajectory optimizer to smooth and adjust the trajectory online based on the vehicle kinematic model. Moreover, the proposed method is simulated and verified in four common dynamic parking scenarios by ACADO Toolkit and QPOASE solver. Findings Compared with the spline optimization method, the results show that the proposed method can generate efficient obstacle avoidance strategies, safe parking trajectories and control parameters such as the front wheel angle and velocity in high-efficient central processing units. Originality/value It is aimed at improving the robustness of automatic parking system and providing a reference for decision-making in a dynamic environment.

Solar sail optimal maneuvers for heliocentric orbit apse line rotation

The aim of this work is to investigate the performance of a solar sail-based spacecraft in an optimal apse line rotation maneuver. Considering a heliocentric two-body motion and a low-performance solar sail with an ideal force model, this study derives the optimal steering law that maximizes the final rotation angle of the osculating orbit apse line. Starting from an approximated (Gaussian) form of the Lagrange’s planetary equations, the achievable argument of periapsis and the required flight times are obtained in a parametric way, for given values of parking orbit eccentricity and sail (reference) propulsive acceleration, as the solutions of an optimal control problem. The numerical simulations show that, for sufficiently large values of the orbit eccentricity, the maximum argument of periapsis is roughly proportional to the sail (reference) propulsive acceleration. An approximate locally-optimal steering law is also derived, and the results of the optimization problem are applied to Earth- and Venus-following orbits, where the planets trajectories are assumed to be circular and coplanar with the spacecraft parking orbit.

Scheduling Optimization of Multiple Hybrid-Propulsive Spacecraft for Geostationary Space Debris Removal Missions

To efficiently implement space debris removal missions on geostationary Earth orbit (GEO), a scheduling optimization scheme of multiple hybrid-propulsive servicing spacecraft (SSc) is proposed. In this scheme, a many-to-many task allocation coding model is established to formulate the removal sequences of debris targets with respect to different SSc. And a hybrid-propulsive orbit transfer strategy is developed to implement the debris removal maneuvers to save the propellant consumption and removal time simultaneously via using both electric and chemical propulsion systems. Then, the multiple GEO space debris removal scheduling optimization problem is formulated to minimize the total propellant consumption by exploring the optimal removal sequences and orbit maneuver parameters, subject to the available velocity increment, removal time, and many-to-many task allocation constraints. To effectively solve the scheduling optimization problem involving discrete-continuous mixed variables, a clustering based adaptive differential evolution algorithm with potential individual reservation (CADE-PIR) is proposed. In CADE-PIR, the k-means clustering method and potential individual reservation mutation strategy are employed to promote the convergence rate and performance of exploration and exploitation. In the end, a real-world GEO space debris removal mission with multiple SSc is investigated to demonstrate the effectiveness and practicality of the proposed optimization scheme.

Stochastic model predictive control-based countermeasure methodology for satellites against indirect kinetic cyber-attacks

The objective of this paper is to provide a stochastic framework to optimally avoid collision between a maneuverable spacecraft and a space object or debris. The satellite collision can be caused through a cyber-attack on a satellite by colliding it with a considered strategic satellite. Consequently, it is highly imperative that critical operational space assets be provided with autonomous collision avoidance systems. The collision avoidance methodology proposed in this paper will reduce the collision probability to an acceptable level and protect the satellite against indirect kinetic cyber-attacks initiated by designing optimal collision avoidance maneuvers using a stochastic model predictive control strategy. The collision probability is estimated using the available historical Two-Line Elements of determined objects, and the model predictive control scheme guarantees the safety of the space close approaches. The proposed and developed collision-avoidance countermeasure methodology is numerically simulated for the collision case study between the Iridium-33 and the Cosmos-2251 satellites. The results demonstrate and illustrate the effectiveness, capabilities, and advantages of our proposed methodology in avoiding probable collisions due to indirect kinetic cyber-attacks.

Bioinspired morphing fins to provide optimal maneuverability, stability, and response to turbulence in rigid hull AUVs

By adopting bioinspired morphing fins, we demonstrate how to achieve good directional stability, exceptional maneuverability, and minimal adverse response to turbulent flow, properties that are highly desirable for rigid hull AUVs, but are presently difficult to achieve because they impose contradictory requirements. We outline the theory and design for switching between operating with sufficient stability that ensures a steady course in the presence of disturbances, with low corrective control action; reverting to high maneuverability to execute very rapid course and depth changes, improving turning rate by 25% up to 50%; and ensuring at all times that angular responses to external turbulence are minimized. We then demonstrate the developments through tests on a 1 m long autonomous underwater vehicle, named Morpheus. The vehicle is capable of dynamically changing its stability-maneuverability qualities by using tuna-inspired morphing fins, which can be deployed, deflected and retracted, as needed. A series of free-swimming experiments and maneuvering simulations, combined with mathematical analysis, led to the design of optimal retractable morphing fins.

Underwater Bearing Only Tracking Using Optimal Observer Maneuver Strategies

This paper considers the problem of tracking a uniform moving source using noisy bearing measurements obtained from a distant observer. Observer trajectory optimization plays a central role in this problem, with the objective to minimize the estimation error of the target state. The Bearing Only Tracking (BOT) of passive targets is mainly focused on the observer maneuver with known trajectories and rarely focused on the future prediction of observer states using adaptive optimization strategies. In this paper, observer paths using one-step ahead optimization based on a performance index are devised which are potentially useful for longer horizon observer trajectory planning in passive tracking. This performance index is the function of source parameters termed as the determinant of Error Covariance Matrix (ECM) which is numerically more efficient than the determinant of Fisher Information Matrix (FIM). The determinant of the FIM requires the calculation of future values for target states and measurements rather than the current values, which is not feasible for Kalman like filters. Therefore, in this paper, the optimization technique is implemented using the state error covariance which is readily available through Kalman filter equations and does require separate numerical calculations. Due to optimal observer maneuver, the performance of the proposed algorithm does not depend on the initial conditions as compared to the conventional tracking methods. The efficiency of the evolutionary algorithm is calculated in terms of range, position and velocity errors and simulation results show 4% fewer estimation errors for ECM based optimization than the determinant of the FIM method.

Optimal Recloser Placement in Distribution System Considering Maneuver Points, Practical Limitations, and Recloser Malfunction

Improving reliability is one of the most critical problems in power distribution networks. Optimal placement of automatic switches improves system reliability. Practical constraints such as maneuver points and the capacity of neighboring feeders are often overlooked. Reliability indices may also be improved by optimizing maneuver point locations considering the capacities of neighboring feeders. In this paper, a new perspective on the problem of recloser placement in the presence of switch disconnectors (sectionneurs) with the ability to disconnect under load is proposed. The limitations of transmission power capacity at maneuver points are also considered in the proposed method. In addition, by proposing a Markov model, the possibility of malfunction of reclosers is considered. The proposed method is applied to two case studies including Roy Billinton Test System (RBTS) and the real distribution network in Mazandaran, Iran. Real system data are collected during the years 2016–2021. The problem is solved for various scenarios. In the case of 12 fully reliable reclosers, taking into account the optimal maneuver points and without any capacity limitations, compared to the allocation of maneuver points at the endpoints of the feeders show a %4.37 increase in the reliability of the real system. Even by considering practical capacity limitations in the maneuver points, this improvement is %3.47. Also, the results show a %6.684 decrease in the system reliability by considering malfunction in the case of 12 reclosers. This underscores the importance of taking into account the reclosers malfunctions in optimal switch placement for better decision making in practice.

A planning tool for optimal three-dimensional formation flight maneuvers of satellites in VLEO using aerodynamic lift and drag via yaw angle deviations

Differential drag is a promising option to control the relative motion of distributed satellites in the Very Low Earth Orbit regime which are not equipped with dedicated thrusting devices. A major downside of the methodology, however, is that its control authority is (mainly) limited to the in-plane relative motion control. By additionally applying differential lift, however, all three translational degrees-of-freedom become controllable. In this article, we present a tool to flexibly plan optimal three-dimensional formation flight maneuvers via differential lift and drag. In the planning process, the most significant perturbing effects in this orbital regime, namely the J2 effect and atmospheric forces, are taken into account. Moreover, varying atmospheric densities as well as the co-rotation of the atmosphere are considered. Besides its flexible and high-fidelity nature, the major assets of the proposed methodology are that the in-and out-of-plane relative motion are controlled simultaneously via deviations in the yaw angles of the respective satellites and that the planned trajectory is optimal in a sense that the overall decay during the maneuver is minimized. Thereby, the remaining lifetime of the satellites is maximized and the practicability and sustainability of the methodology significantly increased. To the best of the authors knowledge, a tool with the given capabilities has not yet been presented in literature. The resulting trajectories for three fundamentally different relevant formation flight maneuvers are presented and discussed in detail in order to indicate the vast range of applicability of the tool.

Rapid accessibility evaluation for ballistic lunar capture via manifolds: A Gaussian process regression application

In this study, a supervised machine learning approach called Gaussian process regression (GPR) was applied to approximate optimal bi-impulse rendezvous maneuvers in the cis-lunar space. We demonstrate the use of the GPR approximation of the optimal bi-impulse transfer to patch points associated with various invariant manifolds in the cis-lunar space. The proposed method advances preliminary mission design operations by avoiding the computational costs associated with repeated solutions of the optimal bi-impulsive Lambert transfer because the learned map is computationally efficient. This approach promises to be useful for aiding in preliminary mission design. The use of invariant manifolds as part of the transfer trajectory design offers unique features for reducing propellant consumption while facilitating the solution of trajectory optimization problems. Long ballistic capture coasts are also very attractive for mission guidance, navigation, and control robustness. A multi-input single-output GPR model is presented to represent the fuel costs (in terms of the ΔV magnitude) associated with the class of orbital transfers of interest efficiently. The developed model is also proven to provide efficient approximations. The multi-resolution use of local GPRs over smaller sub-domains and their use for constructing a global GPR model are also demonstrated. One of the unique features of GPRs is that they provide an estimate of the quality of approximations in the form of covariance, which is proven to provide statistical consistency with the optimal trajectories generated through the approximation process. The numerical results demonstrate our basis for optimism for the utility of the proposed method.

Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

Earth observation satellite (EOS) systems often encounter emergency observation tasks oriented to sudden disasters (e.g., earthquake, tsunami, and mud-rock flow). However, EOS systems may not be able to provide feasible coverage time windows for emergencies, which requires that an appropriately selected satellite transfers its orbit for better observation. In this context, we investigate the orbit maneuver optimization problem. First, by analyzing the orbit coverage and dynamics, we construct three models for describing the orbit maneuver optimization problem. These models, respectively, consider the response time, ground resolution, and fuel consumption as optimization objectives to satisfy diverse user requirements. Second, we employ an adaptive differential evolution (DE) integrating ant colony optimization (ACO) to solve the optimization models, which is named ACODE. In ACODE, key components (i.e., genetic operations and control parameters) of DE are formed into a directed acyclic graph and an ACO is appropriately embedded into an algorithm framework to find reasonable combinations of the components from the graph. Third, we conduct extensive experimental studies to show the superiority of ACODE. Compared with three existing algorithms (i.e., EPSDE, CSO, and SLPSO), ACODE can achieve the best performances in terms of response time, ground resolution, and fuel consumption, respectively.

A Comparative Study to Assess the Effectiveness of Epley’s Maneuver Vs Brandt Daroffs Home Exercises for Management of Benign Paroxysmal Positional Vertigo

Background:Vertigo is one of the most distressing symptom seen in patients encountered in clinical practice by otolaryngologist and neurologist. It results from dysfunction of vestibular system, among which most common is BBPV(benign paroxysmal positional vertigo). BPPV present with short episodes of vertigo lasting for few seconds, usually precipitated by change in head position. In BPPV, otoconia from utricles are thought to collect in semicircular canal making them abnormally gravity sensitive. BPPV is clinical diagnosis on the basis of typical history and Dix Hallpike testing. Dr. T Brandt and Daroff introduced Brandt daroff home exercises based on cupulolithiasis theory. In 1980, John M Epley introduced canalolith repositioning procedure of Epley in the treatment of BPPV. Because of lack of consensus regarding the optimal treatment maneuver, in our study we have compared the effectiveness of Brandt Daroff and Epleys maneuver.

Unmanned Aircraft Collision Detection and Avoidance for Dealing with Multiple Hazards

Collision Detection and Avoidance is one of the critical technologies for fully allowing Unmanned Aerial Systems to fly in civil airspaces. Current methods evaluate only potential conflicts with other aircraft using specific parameters (e.g., time or distance to closest point of approach) that can only be used for pair-wise encounters, not considering the surrounding environment. The present work proposes a new Collision Detection and Avoidance concept to solve short-term conflicts in scenarios characterized by the simultaneous presence of aircraft and other path constraints (i.e., no-fly zones, bad weather areas and terrain) including geo-fencing limitations. Differently from other open literature methods, the proposed algorithm computes two parameters that synthetically describe the conflict hazard level of a given scenario and its possible evolution, independently from the type and the number of surrounding potential threats. Using such indices, a risk evaluation strategy is proposed that detects hazardous situations and generates an optimal maneuver avoiding potential collisions while not causing secondary conflicts. The effectiveness of the proposed algorithm is demonstrated by means of fast-time and real time simulations in some challenging conflict scenarios that cannot be solved by state of the art Detect and Avoid systems.

A singularity‐free online neural network‐based sliding mode control of the fixed‐wing unmanned aerial vehicle optimal perching maneuver

AbstractThe perching behavior of birds is very common in nature, but it is extraordinary for fixed‐wing unmanned aerial vehicles (UAVs), because of post‐stall maneuver. The main problems of the perching maneuver are weak control authority and strong model nonlinearity. This paper proposed a control method to realize the fixed‐wing perch maneuver. For the problem of weak control authority, an optimal trajectory is designed. And a feasible reference trajectory is given under the conditions of model dynamic characteristics and actuator constraints. For the problem of strong nonlinearity, a singularity‐free online neural network‐based sliding mode controller is designed. The simulation test shows that the proposed controller can ensure a reliable perching maneuver under the initial state deviation.

Optimal recovery manoeuvres of racing motorcycles

The focus of this work is on the optimal recovery manoeuvres of racing vehicles, which are here defined as the manoeuvres that need be performed in order to complete the lap in the minimum time, after a driving error occurs. The driving errors considered are speed excess and trajectory deviations, with respect to the baseline conditions. The baseline conditions are those giving the minimum lap time, and are vehicle and track dependent. The practical applications of the methodology proposed include the estimation of the driving errors that can be recovered while remaining within the racetrack borders, and the estimation of the minimum asphalted run-off area that need be provided to recover from driving errors of given magnitude. An example of application is given for a racing motorcycle on the Mugello circuit.

Single-Use and Reusable Flexible Bronchoscopes in Pulmonary and Critical Care Medicine.

Flexible bronchoscopy plays a critical role in both diagnostic and therapeutic management of a variety of pulmonary disorders in the bronchoscopy suite and the intensive care unit. In the set-ting of the ongoing viral pandemic, single-use flexible bronchoscopes (SUFB) have garnered attention as various professional pulmonary societies have released guidelines regarding uses for SUFB given the concern for risk of viral transmission when using reusable flexible bronchoscopes (RFB). In addition to offering sterility, SUFBs are portable, easily accessible, and may be more cost-effective than RFB when considering the potential costs of treating bronchoscopy-related infections. Furthermore, since SUFBs are one time use, they do not require reprocessing after use, and therefore may translate to reduced cleaning and storage costs. Despite these advantages, RFBs are still routinely used to perform advanced diagnostic and therapeutic bronchoscopic procedures given the need for optimal maneuverability, handling, angle of deflection, image quality, and larger channel size for passing of ancillary instruments. Here, we review the published evidence on the applications of single-use and reusable bronchoscopes in bronchoscopy suites and intensive care units. Specifically, we will discuss the advantages and disadvantages of these devices as pertinent to fundamental, advanced, and therapeutic bronchoscopic interventions.

Adaptive Learning for Maximum Takeoff Efficiency of High-Speed Sailboats

This paper presents an optimal takeoff maneuver for an AC75 foiling sailboat competing in the America's Cup. The innovative sailboat design introduces extra degrees of freedom and articulations in the boat that result in nonlinear, high-dimensional, and unstable dynamics. The optimal maneuvers were achieved by exploring out-of-the-box solutions through adaptive control and optimization. We used a high-fidelity sailboat simulator for the data generation process and an adaptive control approach (Jacobian Learning (JL)) to optimize the sailing maneuver. Takeoff is a dynamic sailboat maneuver that involves transitioning the boat from a low-speed in-water status (displacement mode) to a high-speed out-of-water status (foiling mode) via actuation of the sailboat's inputs. We optimized the time for the boat's transitions from displacement mode to foiling mode while maximizing the projection of the velocity (Velocity Made Good (VMG)) in the desired target direction (True Wind Angle (TWA)). Furthermore, we optimized the sailboat's upwind steady-state performance (closed-haul VMG) for varying sailing directions (TWA) and used the optimal TWA to formulate the takeoff. The optimal solution is subject to physical/actuator constraints and the ones enforced to ensure the feasibility of the maneuvers by humans (sailors). The optimal takeoff achieved an average VMG of 7.42 m/s. This maneuver serves as a performance benchmark for the sailors and provides insightful information about the underlying dynamics of the boat.