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In civil engineering, structural optimization seeks an efficient use of material resources and the automatization of the design process of a wide range of structures such as frames, bridges, and other systems. This work develops a novel multiobjective topology optimization process to minimize planar trusses’ weight and strain energy. In the initial stage, an optimized discrete geometry of the ground structure is generated from a continuum design space with general boundary conditions (loads and supports) using the stress trajectories theory. In the final stage, size optimization is performed using the concept of Envelope Pareto Front (EVP), which is obtained from the best solutions provided by three efficient multiobjective metaheuristic algorithms (NSGA-II, MOPSO and AMOSA). The results obtained on a large-scale truss (200 m span continuous bridge) showed that innovative geometries could be found (new connectivity patterns). The generation of an EVP allows getting a more significant number of non-dominated solutions, exploring a broader region of the Pareto front and the two objective functions, achieving greater convergence and diversity than the algorithms’ individual performance. The computation cost of the optimization strategy was satisfactory, which allows its potential implementation in actual large-scale trusses, discovering optimized, innovative solutions for this type of structures. [ABSTRACT FROM AUTHOR]

The recent discovery of Earth's second Trojan asteroid (2020 XL 5 ), which will remain in the vicinity of the Sun–[Earth+Moon] triangular Lagrangian point L 4 for at least 4000 years, has attracted the attention of the scientific community as a remarkable example of those elusive objects that are the witnesses of the first phase of our Solar System. The possibility that an Earth's Trojan asteroid (ETa) may represent a pristine record of the initial conditions of the Solar System formation makes these small objects an interesting target for a robotic exploration mission. This paper analyzes orbit-to-orbit Earth–ETa transfer trajectories of an interplanetary spacecraft propelled by a solar sail. In the last decade, some pioneering space missions have confirmed the feasibility and potentiality of the solar sail concept as a propellantless propulsion system able to convert the solar radiation pressure in a continuous thrust by means of a large, lightweight and highly reflective surface. Using the state-of-the-art level of solar sail technology, this paper studies the performance of a solar-sail-based transfer trajectory toward an ETa from an optimal viewpoint and with a parametric approach. [ABSTRACT FROM AUTHOR]

The global optimization of interplanetary trajectory design is a challenge and tough problem in deep space. To cope with the extreme nonlinearity of the search space and a large number of locally optimal solutions, a novel hybrid algorithm based on success-history based adaptive differential evolution with linear population size reduction (LSHADE), called HLSHADE, is proposed. In HLSHADE, after the global search finds a better solution, a new two-step local search with an adaptive control parameters strategy is designed to enhance the exploitation ability of HLSHADE. The presented method is tested on well-known global trajectory optimization problems (GTOPs) developed by the Advanced Concepts Team of the European Space Agency (ESA). Experimental results have demonstrated the competitive performance of HLSHADE with respect to the three sets of compared algorithms, including LSHADE and ten of its variants, six famous algorithms in the IEEE Competitions on Evolutionary Computation (CEC) and eleven algorithms in the software platform PyGMO developed by the ESA's Advanced Concepts Team. [ABSTRACT FROM AUTHOR]

Helicopters have been playing an increasingly important role in military and civilian activities. The review of helicopter noise reduction is needed to be updated, because new technologies are constantly emerging. Firstly, the mechanism and propagation properties of the rotor (tail rotor) noise are described, and the co‑axial rigid rotor high‑speed helicopter and the tilting rotor are introduced. There are complicated aerodynamic or acoustic interaction in two new configuration helicopters. Secondly, the progress of helicopter flight noise measurement, as the verification approach for low noise design technologies, is demonstrated. Then, the design technologies for silent helicopters are summarized. The passive noise reduction strategies have been intensively implemented, and reduced helicopter radiated noise level greatly. The strategies of variable rotor speed and low noise trajectory are gradually maturing. However, the active control technologies are in the laboratory stage, and need further research on driving devices and control parameters et al. The final part summarizes the state of art for low noise control strategies in helicopters, and outlooks the development trend. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed collision-free control scheme for connected and automated vehicles (CAVs) at a non-signalized intersection. We first divide an intersection area into three sections, i.e., the free zone, the platoon zone, and the control zone. In order to enable the following vehicles to track the trajectory of their leading vehicle in the platoon zone and the control zone, as well as to guarantee the desired distance between any two adjacent vehicles, the distributed platoon controllers are designed. In the control zone, each vehicular platoon is taken as a whole to be coordinated via an intersection coordination unit (ICU). To avoid collision between each pair of the conflicting platoons approaching from different directions, a platoon-based coordination strategy is designed by scheduling the arrival time of each leading vehicle of different platoons. Specially, considering traffic efficiency and fuel economy, the optimal control problem of the leading vehicle is formulated subject to the constraint of allowable minimum arrival time, which is derived from coordination with other approaching platoons. The Pontryagin Minimum Principle (PMP) and phase-plane method are applied to find the optimal control sequences. Numerical simulations show the effectiveness of this scheme. [ABSTRACT FROM AUTHOR]

The performance of spaceborne synthetic aperture radars (SARs) is affected by the Earth's ionosphere. In particular, the ionospheric turbulence causes phase perturbations of the SAR signals, which may lead to image distortions. A convenient way to model those phase perturbations is by means of a phase screen. The latter is an infinitesimally thin layer positioned at a certain elevation above the Earth's surface. The radar signal acquires an instant perturbation once its trajectory intersects the screen. The trajectory is a ray between the antenna and the target, and the magnitude of the perturbation is equal to the screen density at the intersection point. The density is a bivariate function of the coordinates along the screen. The coordinates of a specific intersection point are determined by the ray itself, as well as the screen elevation. Thus, the magnitude of the phase perturbation explicitly depends on the screen elevation. Accordingly, to compensate for the resulting image distortions one should be able to determine the elevation of the screen. In the paper, we develop an algorithm of vertical autofocus that derives this elevation from the received SAR data, given a pair of point scatterers in the target area. The proposed algorithm exploits a modification of the coherent interferometric imaging that was previously employed to reduce the effect of phase errors due to the trajectory uncertainty. In our analysis, we highlight the differences between this case and transionospheric propagation. [ABSTRACT FROM AUTHOR]

To address the time-optimal trajectory planning (TOTP) problem with joint jerk constraints in a Cartesian coordinate system, we propose a time-optimal path-parameterization (TOPP) algorithm based on nonlinear optimization. The key insight of our approach is the presentation of a comprehensive and effective iterative optimization framework for solving the optimal control problem (OCP) formulation of the TOTP problem in the (s , s ˙) -phase plane. In particular, we identify two major difficulties: establishing TOPP in Cartesian space satisfying third-order constraints in joint space, and finding an efficient computational solution to TOPP, which includes nonlinear constraints. Experimental results demonstrate that the proposed method is an effective solution for time-optimal trajectory planning with joint jerk limits, and can be applied to a wide range of robotic systems. [ABSTRACT FROM AUTHOR]

With increasing energy consumption in urban rail transit systems, researchers have paid significant attention to energy-saving train control. In this paper, we propose an effective train trajectory optimization method to reduce the energy consumption based on coasting control, in which coasting control regimes are added to balance running time and energy consumption. For better determining the starting points of coasting control regimes, the whole train running process is divided into several subintervals. Then, aiming to achieve energy efficiency, coasting regimes are added to the subintervals with high energy-saving effects, in which more energy consumption can be reduced with the same running time addition. Based on this, a coasting control method is proposed to generate energy-saving trajectories considering train dynamics, safety, and punctuality. In addition, the proposed method can solve the multisection energy-saving train trajectory optimization problem to obtain optimal running time schemes and related trajectories. Finally, numerical examples based on one of the Beijing metro lines are implemented to verify the effectiveness of the proposed method. The results show that, for the single-section train control problem, the proposed coasting control algorithm can achieve significant energy-saving effects compared to the practical trajectory and calculate energy-saving trajectory in shorter computation times compared to the dynamic programming method. Meanwhile, for the multisection train control problem, energy consumption can be further reduced by optimizing trajectories and running times integratedly. [ABSTRACT FROM AUTHOR]

This paper proposes a novel trajectory planning algorithm to design an end-effector motion profile along a specified path. An optimization model based on the whale optimization algorithm (WOA) is established for time-optimal asymmetrical S-curve velocity scheduling. Trajectories designed by end-effector limits may violate kinematic constraints due to the non-linear relationship between the operation and joint space of redundant manipulators. A constraints conversion approach is proposed to update end-effector limits. The path can be divided into segments at the minimum of the updated limitations. On each path segment, the jerk-limited S-shaped velocity profile is generated within the updated limitations. The proposed method aims to generate end-effector trajectory by kinematic constraints which are imposed on joints, resulting in efficient robot motion performance. The WOA-based asymmetrical S-curve velocity scheduling algorithm can be automatically adjusted for different path lengths and start/end velocities, allowing flexibility in finding the time-optimal solution under complex constraints. Simulations and experiments on a redundant manipulator prove the effect and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In optimizing the energy consumption of a cellular space robot (CSR) climbing in orbit, traditional trajectory planning methods do not include the configuration as a decision variable; thus, it is difficult to obtain an effective energy-optimal climbing strategy for CSRs. To solve this kind of problem, first, we considered the effect of configuration on the kinetic parameters of the robot and established the kinetic equations of a variable-size cellular space robot by combining the spin volume and Lagrange equations. Second, we constructed a higher-order continuous joint trajectory using a cubic spline curve and combined it with a robot dynamics model to establish a robot climbing energy consumption model. Finally, for the energy consumption problem of climbing in cellular space robots, we proposed a comprehensive optimization algorithm considering the configuration and climbing time. Compared with another algorithm in the literature, the algorithm proposed in this paper has stronger global optimization capability and convergence. The simulation results have important theoretical significance and engineering practical value for the study of space robot on-orbit climbing trajectory optimization strategies. [ABSTRACT FROM AUTHOR]

This paper focuses on improving the autonomy and efficiency of fuel-optimal powered landing guidance for reusable rockets considering aerodynamic forces. Deep-learning-based methods are developed to enable online and autonomous operation ability and to avoid the convergence problem encountered by classic indirect and direct optimal control methods. Considering the complex uncertainties of the preceding entry flight and potential unsettling hand-over conditions, a classification network is designed to classify the initial states of landing flights into different categories that correspond to bang–bang or non-bang–bang/singular thrust profiles. Thus, the subsequent online regression network can perform well for a large initial state distribution, and the algorithm adjusts to extensive landing situations. The combined application of classification and regression networks is one of the main contributions of the paper. The offline trained state-action regression networks generate guidance commands according to the real-time rocket state, obtaining a near-optimal landing trajectory. In addition, an online parallel trajectory simulation strategy is proposed to verify the trajectory quality, and an alternative trajectory optimization procedure is embedded into the proposed network-based framework to enhance the safety and accuracy of the guidance algorithm, representing another major contribution. Numerical experiments are presented to evaluate the effectiveness and accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Horizontal well drilling is one of the effective methods to develop shale gas in Longmaxi Formation. The main challenge when drilling wells is to maintain wellbore stability especially across the weak plane of shale formations. In order to solve this problem, a comprehensive geomechanics study was carried out to understand the causes of the wellbore failure and to optimize the most appropriate drilling strategy. In the study, the geomechanical model was conducted by constructing calibrated drilled well profiles using field and laboratory data for the Longmaxi Formation. For the horizontal wells, logging data were used to estimate the rock mechanical properties, overburden pressure, pore pressure and in-situ principal stresses. The results indicated that compared with Mohr-Coulomb, Modified Lade (ML) combined with weak plane (WP) failure criteria are suitable for analyzing the wellbore stability problems to determine optimum mud weight and safe wellbore trajectory for future successful drilling in the study area. Both mud weight and well trajectory affect the wellbore safety, it is recommended to comprehensively consider the influence of mud weight and well trajectory on wellbore instability, which provides practical guidance for drilling engineering and geological design and greatly improve the wellbore safety. [ABSTRACT FROM AUTHOR]

Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population. Despite decades of research on human locomotion, challenges remain in simulating human movement to study the features of musculoskeletal drivers and clinical conditions. Most recent efforts to utilize reinforcement learning (RL) techniques are promising in the simulation of human locomotion and reveal musculoskeletal drives. However, these simulations often fail to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR) and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Inertial Moment Unit (IMU) sensor. The sensor was equipped on the participants' pelvis to capture reference motion data. We also adapted the reward function by leveraging previous research on walking simulations for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means that the simulated human locomotion was more realistic. As a bio-inspired defined cost, IMU data enhanced the agent's capacity to converge during the training process. As a result, the models' convergence was faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quickly and in a broader range of environments, with a better simulation performance. [ABSTRACT FROM AUTHOR]

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis. [ABSTRACT FROM AUTHOR]

In this article, we are interested in optimal aircraft trajectories in climbing phase. We consider the cost index criterion which is a convex combination of the time-to-climb and the fuel consumption. We assume that the thrust is constant and we control the flight path angle of the aircraft. This optimization problem is modeled as a Mayer optimal control problem with a single-input affine dynamics in the control and with two pure state constraints, limiting the Calibrated AirSpeed (CAS) and the Mach speed. The candidates as minimizers are selected among a set of extremals given by the maximum principle. We first analyze the minimum time-to-climb problem with respect to the bounds of the state constraints, combining small time analysis, indirect multiple shooting and homotopy methods with monitoring. This investigation emphasizes two strategies: the common CAS/Mach procedure in aeronautics and the classical Bang-Singular-Bang policy in control theory. We then compare these two procedures for the cost index criterion. [ABSTRACT FROM AUTHOR]

At present, most quadruped robots can move quickly and steadily on both flat and undulating ground; however, natural environments are complex and changeable, so it is important for a quadruped robot to be able to jump over obstacles immediately. Inspired by the jumping movement of quadruped animals, we present aerial body posture adjustment laws and generate animal-like jumping trajectories for a quadruped robot. Then, the bionic reference trajectories are optimized to build a trajectory library of a variety of jumping motions based on the kinematic and dynamic constraints of the quadruped robot. The model predictive control (MPC) method is employed by the quadruped robot to track the optimized trajectory to achieve jumping behavior. The simulations show that the quadruped robot can jump over an obstacle of 40 cm in height. The effectiveness of the animal-like jump control method is verified. [ABSTRACT FROM AUTHOR]

With the increasing demand for manned lunar exploration and deep-space exploration missions, the application value of the three-body orbit in cislunar space becomes increasingly prominent due to its special orbital characteristic. The efficient round-trip versions for manned missions of both the nominal fuel-optimal and emergency transfers between NRHO and the Moon are exploited deeply. The collocation method and indirect optimization technique with constraint gradients are utilized in conjunction with the features of the NRHO configuration to increase the algorithm's accuracy and speed. The required initial value and suitable collocation point can be determined quickly based on a global search strategy and Lambert algorithm. To highlight the effectivity of the design scheme and aim at global lunar surface exploration, typical scenarios of lunar sites at different latitudes and longitudes are investigated with various time of flight, which show that the difference of fuel consumption varies from a few kilograms to hundreds of kilograms. Meanwhile, the relations among Moon landing sites, transition time and fuel consumption are analyzed and the advantages and disadvantages of NRHO in lunar exploration are compared. The design results have some reference value for the lunar landing vehicle's deployment in the near-Moon space, as well as the round-trip transfer mission and key parameter selection. • Fuel-optimal round-trip transfers between NRHOs and LLOs of different sizes are deeply studied. • The benefits of the Lambert algorithm and a global search strategy are combined. • The indirect optimization method with incorporated analytical gradients is applied. • Trajectory characteristics between the sample NRHOs and LLOs with varied latitudes and longitudes are investigated. • The relations among moon landing sites, transition time and fuel consumption are analyzed. • The advantages and disadvantages of NRHO in lunar exploration are compared. [ABSTRACT FROM AUTHOR]

Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an intelligent 3D indoor localization framework that uses the integration of Wi-Fi, Bluetooth Low Energy (BLE), quick response (QR) code, and micro-electro-mechanical system sensors (the 3D-WBQM framework). An enhanced inertial odometry was developed for accurate pedestrian localization and trajectory optimization in indoor spaces containing magnetic interference and external acceleration, and Wi-Fi fine time Measurement stations, BLE nodes, and QR codes were applied for landmark detection to provide an absolute reference for trajectory optimization and crowdsourced navigation database construction. In addition, the robust unscented Kalman filter (RUKF) was applied as a generic integrated model to combine the estimated location results from inertial odometry, BLE, QR, Wi-Fi FTM, and the crowdsourced Wi-Fi fingerprinting for large-scale indoor positioning. The experimental results indicated that the proposed 3D-WBQM framework was verified to realize autonomous and accurate positioning performance in large-scale indoor areas using different location sources, and meter-level positioning accuracy can be acquired in Wi-Fi FTM supported areas. [ABSTRACT FROM AUTHOR]

Coherent motions depict the individuals' collective movements in widely existing moving crowds in physical, biological, and other systems. In recent years, similarity-based clustering algorithms, particularly the Coherent Filtering (CF) clustering approach, have accomplished wide-scale popularity and acceptance in the field of coherent motion detection. In this work, a tracklet-before-clustering initialization strategy is introduced to enhance coherent motion detection. Moreover, a Hierarchical Tracklet Association (HTA) algorithm is proposed to address the disconnected KLT tracklets problem of the input motion feature, thereby making proper trajectories repair to optimize the CF performance of the moving crowd clustering. The experimental results showed that the proposed method is effective and capable of extracting significant motion patterns taken from crowd scenes. Quantitative evaluation methods, such as Purity, Normalized Mutual Information Index (NMI), Rand Index (RI), and F-measure (Fm), were conducted on real-world data using a huge number of video clips. This work has established a key, initial step toward achieving rich pattern recognition. [ABSTRACT FROM AUTHOR]

Truck platooning is considered as a promising approach to improve traffic stability and to reduce fuel consumption and emissions on freeways. This article develops a two-stage trajectory planning model for cooperative and automated trucks (CATs) traveling on freeways to minimize overall fuel consumption considering platooning time and velocity change. In the first stage, five different strategies are proposed to determine the permutation of the platoon given the initial states and travel information of CATs. Once the feasibility is confirmed, the model is formulated as a mixed-integer programming to optimize the trajectories of all CATs on the entire freeway section. The results from the numerical experiments show that the platooning time optimization (PTO) strategy has superior performance in optimizing overall platooning time. Moreover, the velocity change under the condition of maximum platooning time is generally lower for the PTO and the passing time minimum strategies. These characteristics make the PTO strategy combined with the trajectory optimization program a suitable application for cooperative truck platooning management. [ABSTRACT FROM AUTHOR]

The energy-optimal planning of robot trajectory based on dynamics is studied in this paper. We mainly focus on the trajectory optimization problem of industrial robots, which can reduce the energy needed by the robot and produce a smooth trajectory. An optimal path for energy consumption is designed to achieve the kinematic and dynamic constraints. The trajectory planning problem of industrial robot is changed into a nonlinear optimization problem satisfying the constraints. The objective function is discredited, and the sequential quadratic programming (SQP) method is implemented to solve the optimal problem of the robot trajectory energy consumption. First, we verify the trajectory planning under different loads on a two-link manipulator. Applications to a 6-DOF manipulator are implemented to demonstrate the applicability of the proposed method. It is further demonstrated that the energy consumption of the trajectory is significantly reduced compared with that of the quintic polynomial trajectory. The quintic polynomial method energy consumption is 615.50 J, and that of the optimal trajectory is 529.62 J. The energy consumption is reduced by 85.88 J, and the energy consumption reduction rate is up to 13.95%, which obviously proves that this method is practicable. Meanwhile, an obvious 82.3% decrease of energy consumption is obtained in the jerk of the optimal energy trajectory, which makes the manipulator to smoothly run. [ABSTRACT FROM AUTHOR]

We optimized the launch trajectory of an laser-sustained-plasma-type (LSP) laser propulsion rocket at an altitude of 500 km by considering transmission efficiency. The launch cost was calculated based on the results of trajectory calculations. The transmission loss was considered to be the effect of atmospheric turbulence and focusing the performance of the lens. In the optimization problem, the Legendre-Gauss-Lobatto (LGL) method was used for discretization, and the sequential quadratic programming method was used to solve the optimization problem. The payload ratio was 18.0% with a laser power of 200 kW. In the launch calculation, the costs of the laser, electricity, propellant, and airframe were considered for the experience curve effect. The calculation indicated the cost of 267,000 launches was 29.1% of the cost of chemical propulsion. [ABSTRACT FROM AUTHOR]

This paper aims to address the obstacle avoidance problem of autonomous underwater vehicles (AUVs) in complex environments by proposing a trajectory planning method based on the Gauss pseudospectral method (GPM). According to the kinematics and dynamics constraints, and the obstacle avoidance requirement in AUV navigation, a multi-constraint trajectory planning model is established. The model takes energy consumption and sailing time as optimization objectives. The optimal control problem is transformed into a nonlinear programming problem by the GPM. The trajectory satisfying the optimization objective can be obtained by solving the problem with a sequential quadratic programming (SQP) algorithm. For the optimization of calculation parameters, the cubic spline interpolation method is proposed to generate initial value. Finally, through comparison with the linear fitting method, the rapidity of the solution of the cubic spline interpolation method is verified. The simulation results show that the cubic spline interpolation method improves the operation performance by 49.35% compared with the linear fitting method, which verifies the effectiveness of the cubic spline interpolation method in solving the optimal control problem. [ABSTRACT FROM AUTHOR]

This paper considers the problem of minimum-time smooth trajectory planning for wheeled mobile robots. The smooth path is defined by several Bézier curves and the calculated velocity profiles on individual segments are minimum-time with continuous velocity and acceleration in the joints. We describe a novel solution for the construction of a 5th order Bézier curve that enables a simple and intuitive parameterization. The proposed trajectory optimization considers environment space constraints and constraints on the velocity, acceleration, and jerk. The operation of the trajectory planning algorithm has been demonstrated in two simulations: on a racetrack and in a warehouse environment. Therefore, we have shown that the proposed path construction and trajectory generation algorithm can be applied to a constrained environment and can also be used in real-world driving scenarios. [ABSTRACT FROM AUTHOR]

During oil and gas exploitation, wellbore stability has always been the focus of many scholars. Instability of wellbore will lead to washouts, breakout, stuck pipe, mud loss, and so on, even the abandon of the well. Shale rock is widely distributed in nature and must be taken into account in some rock engineering applications. In order to evaluate the strength anisotropy and the influence of moisture content of the wellbore integrity, the LMX outcrops are divided into three groups to carry out triaxial strength experiment. Based on the fitting method of collaborative search, the strength parameters of different moisture contents are determined; then, the effect of strength anisotropy and the moisture content of the LMX reservoir on wellbore collapse pressure and wellbore integrity are investigated. The results show that shale strength, especially cohesion, is more sensitive to the influence of water. With the increase of water content, the inclination angle of the minimum value obtained by shale strength is deviated to the left, and the collapse pressure and instability area increase significantly. At the same time, it is necessary to adjust the direction of borehole drilling to avoid the occurrence of the zone of bedding slip instability in the direction of the minimum in situ stress, and the inversion of in situ stress should be corrected by considering the effect of bedding on the location of the maximum in situ stress. The study on the effects of bedding and water content on the instability zone can provide suggestions and guidance for the optimization of borehole trajectory, the control of bottom hole safety pressure, the inversion of in situ stress, and the drilling and completion of wells. [ABSTRACT FROM AUTHOR]

Over the past few years, with the rapid increase in the number of natural disasters, the need to provide smart emergency wireless communication services has become crucial. Unmanned aerial Vehicles (UAVs) have gained much attention as promising candidates due to their unprecedented capabilities and broad flexibility. In this paper, we investigate a UAV-based emergency wireless communication network for a post-disaster area. Our optimization problem aims to optimize the UAV's flight trajectory to maximize the number of visited ground users during the flight period. Then, a dual cost-aware multi-armed bandit algorithm is adopted to tackle this problem under the limited available energy for both the UAV and ground users. Simulation results show that the proposed algorithm could solve the optimization problem and maximize the achievable throughput under these energy constraints. [ABSTRACT FROM AUTHOR]

We present a method for designing and optimizing Earth–Mars cycler orbits for the Solar System Pony Express (SSPE) mission concept. The SSPE is aimed at augmenting the capabilities of the Deep Space Network (DSN) by enabling high latency and high bandwidth communications through the use of interplanetary data mules. The SSPE will leverage a network of smallsats, outfitted with optical laser communications, and by exploiting cycler orbits it will perform flybys of Mars at least once a year, retrieving 1–3 petabits of data per flyby from missions already operating at the planet. This data will then be downlinked to Earth during the next Earth flyby, also occurring about once a year. The SSPE satellites are launched as a rideshare payload with another Mars-bound mission. Each data mule flying in the SSPE network is equipped with a low-thrust propulsion system that is used for cycler orbit injection and for targeting flybys of Earth and Mars. Cycler orbits are first computed using a patched conic approximation and impulsive thrust. These candidate solutions are then transitioned into a higher-fidelity ephemeris model, and using an indirect optimization method, fuel-optimal low-thrust transfers are computed between each pair of flybys. We present three high-fidelity candidate solutions where a 500 kg data mule is inserted into an Earth–Mars cycler orbit using the NASA Evolutionary Xenon Thruster (NEXT). In all three cases, the results show that the fuel consumption is feasible from a mission design perspective and that solutions obtained in the patched conics model can be successfully transitioned to the ephemeris model using low-thrust propulsion. • Data mule spacecraft flying on Earth–Mars cycler orbits can be used to enhance the capabilities of the Deep Space Network (DSN). • Earth–Mars cycler orbits computed in a patched-conics model are a good initial guess to compute similar cycler orbits in the ephemeris model. • Low-thrust propulsion can be leveraged to design fuel-optimal cycler trajectories. • The propellant mass required to utilize the Earth–Mars cycler orbits is compatible with ESPA-class satellites (< 500 kg). [ABSTRACT FROM AUTHOR]

In recent years, many wall-climbing robots have been developed in the field of petrochemical storage tank maintenance. However, it is difficult for most of them to be widely used due to common problems such as poor adsorption capacity, poor adaptation to elevation, and low trajectory tracking accuracy. In order to solve the problem of the robot not being able to achieve high-precision operation on curved surfaces, a new wall-climbing robot system is designed. Based on the magnetic wheel adsorption method, a passive adaptive motion mechanism that can adapt to walls with different curvatures is proposed. In order to improve the trajectory tracking accuracy of the wall-climbing robot, the kinematic model of the wall-climbing robot is simplified, a velocity compensation controller is designed, and the stability of the controller is proved by introducing the Lyapunov equation. Through experiments, the controller designed in this paper is compared with the conventional controller to verify the effectiveness and superiority of the controller. The experimental results show that the robot can move safely and stably on curved surfaces, with improved tracking accuracy and reduced trajectory deviation caused by response time lag, and meets the maintenance operation requirements of wall-climbing robots. [ABSTRACT FROM AUTHOR]

The reachability assessment of low-thrust spacecraft is of great significance for orbital transfer, because it can give a priori criteria for the challenging low-thrust trajectory design and optimization. This paper proposes an approximation method to obtain the variation maximum of each orbital element. Specifically, two steps organize the contribution of this study. First, combined with functional approximations, a set of analytical expressions for the variation maxima of orbital elements over one orbital revolution are derived. Second, the secular approximations for the variation maxima of the inclination and the right ascension of the ascending node are derived and expressed explicitly. An iterative algorithm is given to obtain the secular variation maxima of the other orbital elements the orbital elements other than the inclination and right ascension of the ascending node. Numerical simulations for approximating the variation maxima and a preliminary application in estimation of the velocity increment are given to demonstrate the efficiency and accuracy of the proposed method. Compared with the indirect method used alone for low-thrust trajectory optimization, the computation burden of the proposed method is reduced by over five orders of magnitude, and the computational accuracy is still high. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose an environmentally adaptive trajectory planning system considering the dynamic characteristics of unmanned aerial vehicles (UAVs) and the distance between obstacles and the UAV. The system generates a smooth and safe flight trajectory online. Design/methodology/approach: First, the hybrid A* search method considering the dynamic characteristics of the quadrotor is used to find the collision-free initial trajectory. Then, environmentally adaptive velocity cost is designed for environment-adaptive trajectory optimization using environmental gradient data. The proposed method adaptively adjusts the autonomous flight speed of the UAV. Finally, the initial trajectory is applied to the multi-layered optimization framework to make it smooth and dynamically viable. Findings: The feasibility of the designed system is validated by online flight experiments, which are in unknown, complex situations. Practical implications: The proposed trajectory planning system is integrated into a vision-based quadrotor platform. It is easily implementable onboard and computationally efficient. Originality/value: A hybrid A* path searching method is proposed to generate feasible motion primitives by dispersing the input space uniformly. The proposed method considers the control input of the UAV and the search time as the heuristic cost. Therefore, the proposed method can provide an initial path with the minimum flying time and energy loss that benefits trajectory optimization. The environmentally adaptive velocity cost is proposed to adaptively adjust the flight speed of the UAV using the distance between obstacles and the UAV. Furthermore, a multi-layered environmentally adaptive trajectory optimization framework is proposed to generate a smooth and safe trajectory. [ABSTRACT FROM AUTHOR]

This paper considers the physical-layer security of information transmission in an unmanned aerial vehicle (UAV) multi-user communication system, where a UAV communicates with multiple users on the ground, with the presence of an eavesdropper on the ground trying to intercept the confidential information sent from the UAV to the users. The channels between the UAV and all ground nodes are modeled as more accurate probabilistic LoS (line-of-sight) channels. This paper studies maximizing the minimum average secrecy rate of all users by jointly designing the three-dimensional (3D) trajectory of the UAV and the user scheduling, under the constraints of UAV fight conditions and the discrete user scheduling parameters. To solve the optimization design problem for the UAV secure communication system under the probabilistic LoS channel model, it first decoupled it into three sub-problem, then solved the three sub-problems one by one, by using the integer relaxation and successive convex approximation methods, and finally obtained the 3D flight trajectory of UAV by using the block coordinate descent method. Based on the instantaneous channel state information obtained during the UAV’s flight, it further proposed a real-time optimization algorithm for UAV speed and user scheduling. This algorithm formulated the optimization problem into a linear programming problem and solved it by using the interior point method. Simulation results show that compared to the existing benchmark algorithms, the trajectory optimization of UAV based on the probabilistic LoS channel model can significantly improve the secrecy rate of the system, and the proposed real-time optimization algorithm for speed and user scheduling can further improve the secrecy rate performance. [ABSTRACT FROM AUTHOR]

Trajectory planning is an important issue for manipulators and robots. To get a optimal trajectory, many constraints including actuators specifications, motion range of joints, workspace limitations, etc, and many objectives including the shortest time, the shortest distance, the lowest energy consumption, the minimum oscillations, obstacles-avoiding, etc, should be considered both. In this paper, firstly, the forward kinematics and inverse kinematics of a five axis manipulator are deduced. And, a simple method to choose one appropriate solution from multi solutions of inverse kinematics is proposed. Secondly, an easy-implemented optimization method of trajectory planning is proposed based on seventh order polynomial interpolation, event-trigger mechanism and conditional proportional control (P control). The proposed optimization method can capture the time optimal trajectory, and the actuators specifications including velocity, acceleration of motor can be guaranteed as well. Thirdly, comparative simulations and experiments validate the effectiveness and efficiency of proposed optimization method. The research provides an insight for the application of trajectory optimization on the micro controller with low computing capability and high real-time performance requirement. [ABSTRACT FROM AUTHOR]

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models. [ABSTRACT FROM AUTHOR]

UAVs are widely used for aerial reconnaissance with imaging sensors. For this, a high detection performance (accuracy of object detection) is desired in order to increase mission success. However, different environmental conditions (negatively) affect sensory data acquisition and automated object detection. For this reason, we present an innovative concept that maps the influence of selected environmental conditions on detection performance utilizing sensor performance models. These models are used in sensor-model-based trajectory optimization to generate optimized reference flight trajectories with aligned sensor control for a fixed-wing UAV in order to increase detection performance. These reference trajectories are calculated using nonlinear model predictive control as well as dynamic programming, both in combination with a newly developed sensor performance model, which is described in this work. To the best of our knowledge, this is the first sensor performance model to be used in unmanned aerial reconnaissance that maps the detection performance for a perception chain with a deep learning-based object detector with respect to selected environmental states. The reference trajectory determines the spatial and temporal positioning of the UAV and its imaging sensor with respect to the reconnaissance object on the ground. The trajectory optimization aims to influence sensor data acquisition by adjusting the sensor position, as part of the environmental states, in such a way that the subsequent automated object detection yields enhanced detection performance. Different constraints derived from perceptual, platform-specific, environmental, and mission-relevant requirements are incorporated into the optimization process. We evaluate the capabilities of the sensor performance model and our approach to sensor-model-based trajectory optimization by a series of simulated aerial reconnaissance tasks for ground vehicle detection. Compared to a variety of benchmark trajectories, our approach achieves an increase in detection performance of 4.48% on average for trajectory optimization with nonlinear model predictive control. With dynamic programming, we achieve even higher performance values that are equal to or close to the theoretical maximum detection performance values. [ABSTRACT FROM AUTHOR]

With the massive application of unmanned aerial vehicles (UAVs) in disaster relief, fire protection, security and other fields, jamming countermeasures to UAVs in airports and competition venues have become the core link of security in high‐level matches and important places. The existing jamming sources are mainly single point jamming. Due to the shielding of buildings, it is impossible to fully cover the venue, and the existing suppressed jamming will affect the communication broadcasting. Here, a distributed optimal jamming method for massive UAVs is proposed. Through the identification and positioning of massive UAVs, using the location information of distributed jamming sources, the UAV jamming architecture based on satellite navigation is constructed. Combined with tabu search artificial bee colony theory, the distributed optimal jamming of UAVs is realized, which solves the problems of few jamming targets, slow speed and low success rate of the existing optimal jamming methods. The proposed method is compared with the existing methods in three aspects: communication frequency, jamming trajectory deviation and optimization speed. The simulation results show that the proposed method effectively improves the jamming success rate and jamming speed and can meet the application requirements of massive UAV jamming in the region. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) can be deployed as aerial wireless base stations which dynamically cover the wireless communication networks for Ground Users (GUs). The most challenging problem is how to control multi-UAVs to achieve on-demand coverage of wireless communication networks while maintaining connectivity among them. In this paper, the cooperative trajectory optimization of UAVs is studied to maximize the communication efficiency in the dynamic deployment of UAVs for emergency communication scenarios. We transform the problem into a Markov game problem and propose a distributed trajectory optimization algorithm, Double-Stream Attention multi-agent Actor-Critic (DSAAC), based on Multi-Agent Deep Reinforcement Learning (MADRL). The throughput, safety distance, and power consumption of UAVs are comprehensively taken into account for designing a practical reward function. For complex emergency communication scenarios, we design a double data stream network structure that provides a capacity for the Actor network to process state changes. Thus, UAVs can sense the movement trends of the GUs as well as other UAVs. To establish effective cooperation strategies for UAVs, we develop a hierarchical multi-head attention encoder in the Critic network. This encoder can reduce the redundant information through the attention mechanism, which resolves the problem of the curse of dimensionality as the number of both UAVs and GUs increases. We construct a simulation environment for emergency networks with multi-UAVs and compare the effects of the different numbers of GUs and UAVs on algorithms. The DSAAC algorithm improves communication efficiency by 56.7%, throughput by 71.2%, energy saving by 19.8%, and reduces the number of crashes by 57.7%. [ABSTRACT FROM AUTHOR]

A high-precision online trajectory optimization method combining convex optimization and Radau pseudospectral method is presented for the large attitude flip vertical landing problem of a starship-like vehicle. During the landing process, the aerodynamic influence on the starship-like vehicle is significant and non-negligible. A planar landing dynamics model with pitching motion is developed considering that there is no extensive lateral motion modulation during the whole flight. Combining the constraints of its powered descent landing process, a model of the fuel optimal trajectory optimization problem in the landing point coordinate system is given. The nonconvex properties of the trajectory optimization problem model are analyzed and discussed, and the advantages of fast solution and convergence certainty of convex optimization, and high discretization precision of the pseudospectral method, are fully utilized to transform the strongly nonconvex optimization problem into a series of finite-dimensional convex subproblems, which are solved quickly by the interior point method solver. Hardware-in-the-loop simulation experiments verify the effectiveness of the online trajectory optimization method. This method has the potential to be an online guidance method for the powered descent landing problem of starship-like vehicles. [ABSTRACT FROM AUTHOR]

This paper mainly studies the optimization of the patrol route of a fire inspection robot, based on analytic hierarchy process (AHP) and fire source detection. First, the A* algorithm is improved based on two aspects of the heuristic function and of the obstacle boundary setting, while the suboptimal path is obtained in MATLAB. Next, the path planned according to the improved A* algorithm is smoothed and optimized, by means of gradient descent method, Bezier curve and B-spline curve, while the index parameters are optimized by means of MATLAB simulation. In view of the simulation results, the trajectory optimization performance index evaluation system, established by five decision criteria, including running time, path length, ride comfort, no-collision effect and quadratic optimization space, is put forward. The three kinds of optimization methods are analyzed qualitatively and quantitatively, and the results show that, in the total hierarchical ranking, the B-spline curve trajectory optimization scheme has the largest weight and is more important than the other two schemes. Finally, the superiority of B-spline curve is verified experimentally. [ABSTRACT FROM AUTHOR]

In order to make the grid-connected composite device (GCCD) controller meet the requirements of different operating modes and complex working conditions of power grid, this paper proposes to introduce sliding mode control (SMC) into GCCD controller. Firstly, the mathematical model of MMC converter is established, and the sliding mode controller is designed based on the SMC principle. Then, aiming at the problems of complex controller structure and difficult parameter tuning in multiple modes of the GCCD, this paper proposes a controller parameter optimization method based on improved Month Flame optimization (IMFO) algorithm. This method improves the MFO algorithm by introducing good point set (GPS) initialization and Levy flight strategy, which accelerates the convergence speed of the algorithm while avoiding falling into local optimization, and realizes the optimization of converter controller parameters. Under a variety of standard test functions, the advantages of the proposed IMFO algorithm are verified by comparing it with the traditional algorithm. Finally, in order to realize the automatic tuning of control parameters, the Python–PSCAD joint simulation method is studied and implemented. Taking the comprehensive integral of time and absolute error (CITAE) index as the objective function, the parameters of the sliding mode controller are optimized. The simulation results show that the controller parameters optimized by the IMFO algorithm can make the GCCD have better dynamic performance. [ABSTRACT FROM AUTHOR]

This paper studies the anti-jamming problem of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks in the presence of a jammer under the accurate probabilistic line-of-sight (LoS) model. Our goal is to maximize the information collection throughput of the system under the assumption that only the jammer's approximate location is known. To this end, we formulate a throughput maximization problem by optimizing the UAV trajectory, the IoT devices' transmit power, and communication scheduling under the accurate real-time probabilistic LoS channel. However, the proposed optimization problem is non-convex and coupled, and hence intractable to be solved. In order to tackle the problem, a robust iterative algorithm is proposed by leveraging the block coordinate descent (BCD) method, the successive convex approximation (SCA) technology, the difference of convex (D.C) programming approach, and the S -procedure. Extensive simulation results show that our proposed algorithm significantly improves the system throughput while achieving a practical anti-jamming effect compared with the benchmark algorithms. [ABSTRACT FROM AUTHOR]

The coordinated motion between the press and the feeding mechanism directly determines the production efficiency of the high-speed stamping line. In order to generate the high-performance trajectory of the feeding mechanism, this paper investigates the optimization of the trajectory with the local variable period. Based on the quintic B-spline curve and normal distribution, the smooth interpolation method of variable-time interval is proposed to generate the collision-free and energy-jerk-minimal trajectory with variable-time intervals. The advantage of the proposed method is that it can make the feeding mechanism transition smoothly between districts of variable-time and fixed-time intervals. It is beneficial to avoid re-performing the entire process of trajectory planning. ADAMS and actual experiments are used to validate the effectiveness of the proposed method. Results show that the proposed method can maintain the high performance of the initial trajectory, and there is no sharp point in the displacement-time and velocity-time curves. The investigation provides a new direction for the direct generation of local variable-period trajectories in the multi-machine coordination of the stamping line. [ABSTRACT FROM AUTHOR]

This paper proposes a roundabout controller that operates in a connected and automated vehicle (CAV) environment. The proposed controller bears the following features: (1) improves the mobility and capacity of roundabouts, (2) has the potential for real-world implementation, and (3) is easy to upgrade for partially connected and automated traffic. The proposed controller is evaluated against a conventional roundabout controller to quantify its performance. The results show that the proposed controller is able to improve throughput by approximately 14% and reduce delays ranging from 66% to 94%. The computational time of the proposed controller was demonstrated to be constantly less than 0.02 s across all traffic demands. This confirms the potential of the proposed controller for real-world application. [ABSTRACT FROM AUTHOR]

The UT Austin team presents their methodology and result for GTOC11, constructing a hypothetical Dyson ring using asteroids encountered by 10 motherships departing from Earth. A pathfinding algorithm for the mothership is designed using a fast lookup table and a robust Lambert solver. Sequencing of unique mothership itineraries is performed by approximating the mass delivered to the ring via a linear data-based estimator trained on simulated asteroid-to-ring trajectories. A detailed derivation for the necessary boundary conditions of the constant-thrust-acceleration indirect optimization problem is presented, with key insights about the magnitudes of the co-states. An indirect boundary value solver finds feasible asteroid-to-ring trajectories for each of the 12 phasing locations in a high-performance parallel computing environment. A genetic algorithm informs the sequencing for the ring station arrival order that gives the highest performance index value. UT Austin's final submitted solution placed 4th in the competition, with a performance index value of 5885.5, 235 asteroids collected, and a minimum build-station mass of 1.1328 × 1 0 15 kg. • The UT Austin team presents their 4th place solution for GTOC11. • Multiple-leg impulsive trajectories are generated using spatial/temporal grid lookups. • Incoming and outgoing impulsive maneuvers are efficiently combined between legs. • A linear estimator aids selection of multiple-leg impulsive trajectory itineraries. • A genetic-algorithm-based scheduler delivers equal asteroid mass to 12 build stations. [ABSTRACT FROM AUTHOR]

This paper presents the crucial method for the results found at Beijing Institute of Technology and China Academy of Space Technology in the 11th Global Trajectory Optimization Competition (GTOC-11). To build 12 solar-power stations placed in the "Dyson Ring" orbit around the Sun, GTOC-11 focuses on the flyby trajectory design, low-thrust trajectory optimization, and target assignment problem. The mission with time and state constraints causes many difficulties in the design and optimization, which is performed in the following four steps. The first step is to design the orbital elements of the "Dyson Ring" by the grid search method. An approximate model is utilized to evaluate the minimum transfer time for each candidate at first, and the accurate solutions are obtained by the indirect method. The second step involves the Mother Ship trajectories design. A large quantity of earth-to-asteroid and asteroid-to-asteroid subsequences are generated by parallel beam search. The complete trajectories of Mother Ship are designed by connecting the subsequences using beam search. The third step involves the station assignment problem, which is required to design the asteroid transfer device activation epoch and the target station assignment. Based on the results above, the last step involves the rendezvous trajectories optimization to obtain the accurate thrust direction profile. Finally, the submitted and post-competition results are reported. • The simplified and accurate model are proposed to obtain the optimal transfer time. • The parallel beam search is developed to reduce the computational burden. • The station assignment problem is solved by coordinate descent method. [ABSTRACT FROM AUTHOR]

The National University of Defense Technology and the Xi'an Satellite Control Center organized the 11th edition of the Global Trajectory Optimization Competition (GTOC11) in 2021. The GTOC11 problem was created as a link between the ninth and tenth editions of the Global Trajectory Optimization Competition to bridge the gap between the planetary and galactic civilizations by introducing an intermediate stellar civilization scenario. The problem involves the construction of a Dyson sphere, a theoretical mega-structure that encircles a star with platforms orbiting in a tight formation to capture the maximum energy from it. Challenges in astrodynamics including the construction-orbit selection, combinatorial flyby of multiple asteroids, and mass distribution among stations were considered in the Dyson sphere design. A total of 94 teams registered for the competition, of which 25 teams provided solutions and passed automatic verification on the website. In this article, we describe the selection of the problem and its design process. In addition, an overview of the entire competition and an analysis of its results are presented. • NUDT and XSCC organized the 11th edition of the GTOC. • The problem of the GTOC-11 was designed to build a preliminary "Dyson Sphere". • A total of 94 teams registered this competition, and 25 of them returned solutions. [ABSTRACT FROM AUTHOR]

This paper presents the methods and the results submitted by the team of Harbin Institute of Technology in the 11th Global Trajectory Optimization Competition (GTOC11). The mission of GTOC11 is to design the Mother Ship trajectories to execute asteroid flybys selected from over 83000 candidates, and then transfer these asteroids to 12 stations to build the "Dyson Ring" while maximizing the total performance index. The building mission is completed by five steps: "Dyson Ring" orbit selection, database creation, Mother Ship trajectory design, building station construction strategy, and asteroid transfer trajectory optimization. The database creation is mainly used to fast estimate the transfer time for the asteroid low-thrust trajectory, and it plays an essential role in the whole mission. Finally, the building of the "Dyson Ring" is realized within the given period. The final index of the submitted result is 5208.3463, which ranked seventh in GTOC11. • To reduce computation burden for database, some nominal orbits are created to estimate transfer time. • The flyby sequence of asteroids is obtained by the heuristic algorithm. • The Bezier shape-based method is used as the initial guess for low thrust trajectory optimization. • To make the asteroids reach the building station simultaneously, an inverse rendezvous method is proposed. [ABSTRACT FROM AUTHOR]

In this work, Team ASRL's solution approach for the 11th Global Trajectory Optimization Competition (GTOC-11) is described. This problem tasked the competing teams with constructing a futuristic Dyson Ring utilizing materials acquired from the asteroid belt. In total, 10 motherships would depart from Earth in the year 2121 and visit as many asteroids as possible. After visiting each asteroid, a low-thrust propulsion module would transfer the material down to the desired final Dyson stations. The final approach utilized a deterministic tree search that involved alternating between fixed time of flight Lambert searches and solutions to the full-fidelity optimal control problem. Once a single tour had been constructed, transfer trajectories were computed for each asteroid to as many of the building stations as possible. After computing a pool of thousands of these completed legs, a bin packing algorithm was used to determine the highest scoring combination of 10 solutions. This search process was implemented in Python using the soon-to-be-released trajectory optimization tool, ASSET. Ultimately, the team finished 5th with a score of 5525.38. • GTOC11 results from the Astrodynamics and Space Research Laboratory (ASRL) team. • Selection of asteroid population subsets based on mean deliverable mass to stations. • Deterministic beam search algorithm with full-fidelity optimal control solutions. • A bin packing algorithm for minimizing variance in final Dyson station mass. [ABSTRACT FROM AUTHOR]

The method used and results found by the team "ASTL-NUAA" from NUAA for the problem of GTOC 11 are presented in the paper. The problem is mainly decomposed into three parts: multiple targets flyby sequence optimization for the Mother Ships, station-building database construction problem of asteroid transfer arcs, and station-building mission planning. Therefore, a three-step optimization framework is proposed to solve the problem. After analyzing the data of all the asteroids and selecting according to the orbital elements and mass of the asteroids, an asteroid candidate set RS1 is chosen as candidate targets for the Mother Ships. A two-level optimization algorithm based on a genetic principle is applied to solve the two-impulse asteroid flyby problem and rapidly determine the asteroid sequence of each Mother Ship. Subsequently, a method of filtering input data for the time-optimal rendezvous problem by the optimal flight time and the corresponding optimal relative phase is proposed to construct a station-building database with less computation burden. The optimal relative phase is obtained by solving free final-phase continuous low-thrust time-optimal problem. The station-building database is composed of the results of continuous low-thrust time-optimal rendezvous problems. At last, a branch-and-bound algorithm based on the greedy strategy is adopted to rapidly get the result by maximizing the minimal mass among the stations. The final score of our solution is 4315.6663, which is obtained after the competition. • The method used and results found by NUAA for the GTOC 11 is presented. • The flyby sequence of asteroids is obtained by the two-level optimization algorithm. • For less computation burden, a filter strategy is proposed to filter the input of time-optimal problem. • To assign asteroids, the branch-and-bound algorithm is applied. [ABSTRACT FROM AUTHOR]

In this paper, the methods and results from Tsinghua University and Shanghai Institute of Satellite Engineering for the 11th Global Trajectory Optimization Competition (GTOC11) are presented. To deal with the complicated "Dyson Sphere" building problem, a three-stage procedure is conducted. First, the pre-analysis is performed to reduce search space. It is found that two-impulse maneuvers between asteroid flybys are near-optimal, the semi-major axis of the "Dyson Ring" should be better at 1.0–1.5 AU, and the larger arrival mass asteroids tend to be selected. Second, the globally optimal trajectory design problem is further divided into two sub-problems, the mothership trajectory design and the asteroid assignment to the "Dyson Ring" power stations. For the first problem, beam search is used to obtain numerous single mothership trajectories based on a pre-constructed flyby trajectory database of 3–8 asteroids. The overall trajectories and asteroids visited are obtained by selecting 10 mothership trajectories with a genetic algorithm. For the second problem, we build a database of optimal rendezvous times for all the 83,453 asteroids at different phase angles to reach power stations of different radii and phase angles, then a greedy algorithm is proposed to obtain the asteroid arrival schedule based on all the asteroids visited by motherships. Finally, local optimization of asteroid sequence and flyby epochs is conducted. The activation time adjustment in combination with indirect continuous-thrust trajectory optimization is used based on the global optimization result. In the final submission, motherships fly by 388 asteroids, and the minimum mass of twelve power stations reaches 94% of the theoretical upper bound, which is defined using the minimum-time orbital transfers with free initial and target phases. • The winning result submitted for GTOC 11 is discussed. • Solving the flyby by the rendezvous in combination with geometric corrections. • Optimizing the flyby times in asteroid chains to solve the time-dependent problems. • An exponential form of the heuristic function is used in beam search. • A low-thrust solution database is constructed to obtain accurate flight time. [ABSTRACT FROM AUTHOR]