Optimal Trajectory Design Research Articles

Trajectory design and transmission optimization for data freshness in UAVs‐assisted WPCNs

Trajectory design and transmission optimization for data freshness in UAVs‐assisted WPCNs

Trajectory optimization for the diving segment of air-breathing supersonic vehicle with multi-objective

Abstract Aiming at the trajectory optimization problem of the dive section of the supersonic vehicle under the comprehensive action of different objective functions, a trajectory optimization design method for multi-optimization objectives of air-breathing supersonic vehicles is proposed. The hp adaptive pseudo-spectrum method is used to optimize the dive trajectory of an air-breathing supersonic vehicle with the maximum flight range and maximum landing velocity under the conditions of process constraints such as overload and dynamic pressure by the weighted summation method. It is proved that this method can solve the comprehensive optimization problem of multiple optimization objectives effectively and provides a new thought for the optimization of related fields.

Fairness-Aware Computation Offloading With Trajectory Optimization and Phase-Shift Design in RIS-Assisted Multi-UAV MEC Network

Fairness-Aware Computation Offloading With Trajectory Optimization and Phase-Shift Design in RIS-Assisted Multi-UAV MEC Network

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

Electric vertical takeoff and landing (eVTOL) aircraft represent a crucial aviation technology to transform future transportation systems. The unique characteristics of eVTOL aircraft include reduced noise, low pollutant emission, efficient operating cost, and flexible maneuverability, which in the meantime pose critical challenges to advanced power retention techniques. Thus, optimal takeoff trajectory design is essential due to immense power demands during eVTOL takeoffs. Conventional design optimizations, however, adopt high-fidelity simulation models in an iterative manner resulting in a computationally intensive mechanism. In this work, we implement a surrogate-enabled inverse mapping optimization architecture, i.e., directly predicting optimal designs from design requirements (including flight conditions and design constraints). A trained inverse mapping surrogate performs real-time optimal eVTOL takeoff trajectory predictions with no need for running optimizations; however, one training sample requires one design optimization in this inverse mapping setup. The excessive training cost of inverse mapping and the characteristics of optimal eVTOL takeoff trajectories necessitate the development of the regression generative adversarial network (regGAN) surrogate. We propose to further enhance regGAN predictive performance through the transfer learning (TL) technique, creating a scheme termed regGAN-TL. In particular, the proposed regGAN-TL scheme leverages the generative adversarial network (GAN) architecture consisting of a generator network and a discriminator network, with a combined loss of the mean squared error (MSE) and binary cross-entropy (BC) losses, for regression tasks. In this work, the generator employs design requirements as input and produces optimal takeoff trajectory profiles, while the discriminator differentiates the generated profiles and real optimal profiles in the training set. The combined loss facilitates the generator training in the dual aspects: the MSE loss targets minimum differences between generated profiles and training counterparts, while the BC loss drives the generated profiles to share analogous patterns with the training set. We demonstrated the utility of regGAN-TL on optimal takeoff trajectory designs for the Airbus A3 Vahana and compared its performance against representative surrogates, including the multi-output Gaussian process, the conditional GAN, and the vanilla regGAN. Results showed that regGAN-TL reached the 99.5% generalization accuracy threshold with only 200 training samples while the best reference surrogate required 400 samples. The 50% reduction in training expense and reduced standard deviations of generalization accuracy achieved by regGAN-TL confirmed its outstanding predictive performance and broad engineering application potential.

Variational Method-Based Trajectory Optimization for Hybrid Airships

Hybrid airships, combining aerodynamic lift and buoyant lift, are efficient near-space aircraft for scientific exploration, observation, and surveillance. Compared to conventional airplanes and airships, hybrid airships offer unique advantages, including stationary hovering and rapid deployment. Due to the different task requirements and strong coupling between flight and environment, trajectory-optimization methods for traditional aircraft are difficult to apply to hybrid airships directly. We propose a trajectory-optimization model based on the variational method to calculate the optimal time and energy paths under weak, uniform, and latitudinal linear wind fields. Our model shows that the influencing factors for the optimization path can be categorized into three types: airship design parameters, wind field parameters, and departure parameters. The result indicates that the optimal time paths are generally straight lines, and the optimal energy paths are piecewise curves with a 24-h cycle under typical hybrid airship design parameters. This work has provided new insight into the trajectory optimization and parameter design of future hybrid airships.

Joint Trajectory Design and Resource Optimization in UAV-Assisted Caching-Enabled Networks with Finite Blocklength Transmissions

In this study, we design and analyze a reliability-oriented downlink wireless network assisted by unmanned aerial vehicles (UAVs). This network employs non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes. In the network, ground user equipments (GUEs) request content from a remote base station (BS), and there are no direct connections between the BS and the GUEs. To address this, we employ a UAV with a limited caching capacity to assist the BS in completing the communication. The UAV can either request uncached content from the BS and then serve the GUEs or directly transmit cached content to the GUEs. In this paper, we first introduce the decoding error rate within the FBL regime and explore caching policies for the UAV. Subsequently, we formulate an optimization problem aimed at minimizing the average maximum end-to-end decoding error rate across all GUEs while considering the coding length and maximum UAV transmission power constraints. We propose a two-step alternating optimization scheme embedded within a deep deterministic policy gradient (DDPG) algorithm to jointly determine the UAV trajectory and transmission power allocations, as well as blocklength of downloading phase, and our numerical results show that the combined learning-optimization algorithm efficiently addresses the considered problem. In particular, it is shown that a well-designed UAV trajectory, relaxing the FBL constraint, increasing the cache size, and providing a higher UAV transmission power budget all lead to improved performance.

Optimal Trajectory Design for Unmanned Aerial Vehicle Cargo Pickup and Delivery System based on Radio Map

Optimal Trajectory Design for Unmanned Aerial Vehicle Cargo Pickup and Delivery System based on Radio Map

Optimal Tilt-Wing eVTOL Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks

Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to address large-scale problems. Therefore, we showcase a novel regression generative adversarial network (regGAN) surrogate for fast interactive optimal takeoff trajectory predictions of eVTOL aircraft. The regGAN leverages generative adversarial network architectures for regression tasks with a combined loss function of a mean squared error (MSE) loss and an adversarial binary cross-entropy (BC) loss. Moreover, we introduce a surrogate-based inverse mapping concept into eVTOL optimal trajectory designs for the first time. In particular, an inverse-mapping surrogate takes design requirements (including design constraints and flight condition parameters) as input and directly predicts optimal trajectory designs, with no need to run design optimizations once trained. We demonstrated the regGAN on optimal takeoff trajectory designs for the Airbus A3 Vahana. The results revealed that regGAN outperformed reference surrogate strategies, including multi-output Gaussian processes and conditional generative adversarial network surrogates, by matching simulation-based ground truth with 99.6% relative testing accuracy using 1000 training samples. A parametric study showed that a regGAN surrogate with an MSE weight of one and a BC weight of 0.01 consistently achieved over 99.5% accuracy (denoting negligible predictive errors) using 400 training samples, while other regGAN models require at least 800 samples.

Optimal UAV Trajectory Design for Moving Users in Integrated Sensing and Communications Networks

Optimal UAV Trajectory Design for Moving Users in Integrated Sensing and Communications Networks

On Optimal Rigid Body Rotation with Internal Forces Application

The article describes the result obtained for the problem of a rigid body’s maximum rotation in a given time interval by moving a movable internal mass. The mass movement is achieved by applying limited force. Previously, similar problems were considered in which the displacements of internal mass were assumed to be kinematic with restrictions on the point’s speed. The obtained result is described by analytical, easily verifiable formulas. The optimal trajectory of the moving mass is a spiral that coils around the center of mass of a rigid body with a frequency increasing to infinity. The obtained numerical results relate to the design of other optimal trajectories that cannot be analyzed analytically.

Optimal splicing of multi-segment analytical trajectories for electric sails

In this paper, the analytical approximation, and the corresponding modified solution of the two-dimensional propelled trajectory of the electric sail with fixed pitch angle are spliced for the optimal design of the transfer orbit. Based on the linear perturbation theory, the analytical approximate propelled trajectory of the electric sail launched from a general heliocentric elliptical orbit can be obtained. Interestingly, this semi-analytic solution is no longer limited to the circular orbit assumption. Therefore, the optimal transfer trajectories between different heliocentric orbits can be obtained by splicing the analytical propelled trajectories with different pitch angles. Significantly, for such a problem that requires a large amount of iterative computation, compared to numerical integration, the analytical solution can greatly reduce the calculation time. In addition, the method of using pitch angles as the main optimization variables can greatly improve optimization efficiency. Moreover, based on the high accuracy of the modified analytical approximation, the optimal trajectory obtained by the proposed method is extremely close to the optimal real solution obtained by the shooting method and numerical integration. For the electric sail with a characteristic acceleration of 0.03 mm/s2, the approximation error of the optimal trajectories in the multi-target mission obtained by the proposed method and the integral shooting method is as low as 10−6∼10−4. For multiple variable targets, the difference of indicators is approximately 10−5∼10−4. Furthermore, compared with numerical integration, the proposed analytical method can reduce the calculation time of multi-target optimization problems by at least 80 %. Notably, with extremely low accuracy loss and calculation time, the proposed method is applicable to the rapid design of optimal transfer trajectory of electric sail-based spacecraft in multi-target mission scenarios.

Geomechanical modeling of ultradeep fault‐controlled carbonate reservoirs and its application, a case of the Fuman Oilfield in Tarim Basin

AbstractTo improve the development efficiency of ultradeep fault‐controlled carbonate reservoirs, the deformation and connectivity mechanism of fractures was revealed through large‐scale rock mechanical experiments. The in situ stress field and fracture activity distribution law of fault‐control carbonate reservoirs were clarified through geomechanical modeling. It is revealed that the fracture activity in different directions and the connectivity of the fracture and cavity in different parts are significantly different, the development effect of different well trajectories is analyzed, and the integrated working method of geological engineering is proposed to scientifically guide the well trajectory design and water injection scheme optimization. The results show that: (1) Large‐scale fractures and high‐angle fracture systems in the deformation of strike–slip faults are the key factors affecting reservoir quality. High‐pressure water injection can activate preexisting fractures, and it can extend on the basis of preexisting fractures, and even generate new fractures, which promote the interconnection of fault‐controlled fracture‐cave bodies in the vertical and horizontal directions. (2) During the process of high‐pressure water injection, the coupling change between mechanics and flow occurs inside the fracture body, the seepage environment is improved, and the oil and gas recovery factor is improved through cyclic lifting. (3) According to the shape and occurrence of the fault body and the dynamic shear deformation connectivity of the fault surface, the best well point and well trajectory of the directional well can be optimized, and the water injection scheme can be optimized. (4) The single‐well production increased by 1.45 × 104 t through high‐pressure water injection, the cumulative oil increase of the same fracture‐cavity body increased by 4.63 × 104 t. This method is highly efficient for ultradeep fault‐controlled oil reservoirs. The development provides a good theoretical basis and technical support, and plays a leading and exemplary role.

Reinforcement learning-based hybrid differential evolution for global optimization of interplanetary trajectory design

The global optimization and design of interplanetary trajectories is one of the most important tasks in deep space exploration. Its search space is often characterized by multi-constraints, extreme non-linearity and sensitivity to initial conditions. To cope with these difficulties, an improved reinforcement learning-based hybrid differential evolution (DE) named RL_HDE is proposed in this paper. In RL_HDE, a novel multi-mutation strategy LSHADE based on an adaptive Q-Learning framework is proposed for global exploration. To further balance the global exploration and local exploitation of RL_HDE, a new parameter adaptive strategy based on Q-Learning is designed to control two trigger parameters. The performance of RL_HDE is verified by the well-known Global Trajectory Optimization Problems (GTOP), which are developed by Advanced Concepts Team of European Space Agency (ESA-ACT). A comparison of RL_HDE with three sets of algorithms, including ten state-of-the-art hybrid evolutionary algorithms, four interplanetary trajectory design algorithms, and seven reinforcement learning-based hybridizations. Experimental statistical results demonstrate that RL_HDE outperforms other competitors in terms of convergence efficiency and accuracy. RL_HDE has better performance for solving complex interplanetary trajectory design problems such as Cassini2 and Messenger-full.

Low-Thrust Transfer Dynamics and Control Between Halo Orbits in the Earth–Moon System by Means of Invariant Manifold

The low-thrust transfer between halo orbits in the Earth-Moon system is studied in this paper. The optimization problem of low-thrust transfer is challenging in the circular restricted three-body problem (CRTBP). Making reasonable use of the three-body dynamics structure can reduce the thrust required for the transfer. The invariant manifolds associated with planar Lyapunov orbits are spliced at first to create homoclinic and heteroclinic connections that can be used as trial solutions for low-thrust transfer. After that, the transfer of spatial halo orbits is addressed using the continuation methods. The Pontryagin maximum principle is used to calculate the optimal control law for the transfer process. Trajectories with various numbers of rotations around the Moon are suitable for transfer between halo orbits with different orientations, depending on the category of the manifold splicing. Finally, a typical MPC controller is designed to track the transfer trajectory in the presence of perturbations. Numerical simulations show the efficiency and practicability of the optimal transfer trajectory design strategy by means of invariant manifold for future lunar exploration missions.

Trajectory Synthesis and Optimization Design of an Unmanned Five-Bar Vegetable Factory Packing Machine Based on NSGA-II and Grey Relation Analysis

To address the problems of the complex structure and single packing trajectory of a packing machine, a hybrid-driven, five-bar packing machine for same-point pickup and different points of release in unmanned plant factories was designed, and a GRA-C method based on grey correlation analysis and CRITIC weighting for the quadratic optimization of Pareto solutions was proposed. According to the agronomic requirements, the original track of the packing machine was designed. The trajectory synthesis of the packing mechanism was completed based on the NSGA-Ⅱ multi-objective optimization algorithm. To reduce the overall size of the five-bar mechanism and to ensure its good motion performance, an optimization model for trajectory synthesis was established, and the optimal solution was obtained via the quadratic optimization of the Pareto front solution. To further improve the motion performance of the mechanism, the angular displacement curve at the secondary trajectory points was fitted. Through a comparative analysis with the solutions of three special points in the Pareto front solution set, it was found that the standard deviation of the angular velocity and the standard deviation of the angular acceleration after the quadratic optimization were 26.07% and 24.42% lower than the average values of the other three groups of solutions, respectively. The final optimization results were used to design the vegetable packaging machine, and the trajectory was found to be in good agreement with the expected trajectory, with a root mean square error of only 0.74.

An analytical cutting force model for elliptical vibration texturing of nano-grating surfaces

Although the direct texturing processes using dynamic cutting depth modulation have been widely demonstrated for generating micro/nano-structured surfaces, the material shearing removal dominates the surface topography generation that inevitably inhibits the machining feasibility of high-aspect textures. This paper proposes a novel nano-grating generation mechanism governed by the combined effect of shearing-based material removal and compressive forming-based deformation in elliptical vibration texturing. A newly dynamic cutting force model is established to reveal its transient cutting mechanics that incorporating the process elastic rebounding behaviors and essential local cutting characteristics in three deformation zones. The model fully considers the complex transient shear angle variation, friction reversal phenomenon on tool-chip interface as well as the unique flank-waviness interaction. A series of texturing experiments are delicately designed to modulate the contribution of material shearing removal and flank compressive forming on nano-grating generation mechanism. The measured dynamic cutting force show good agreements with the simulated results, based on which the model accuracy and main process assumptions are well validated. Furthermore, the relationship between the cutting force characteristics and nano-grating topography is analyzed to guide the optimal elliptical trajectory designs for simultaneously achieving high-quality nano-gratings and smaller dynamic cutting forces. The outcomes of this paper not only lay the scientific foundations for machining capability extension of direct texturing processes, but also contribute to the understandings of combined surface generation mechanisms of other intermittent cutting processes in nano-scale domain.

Optimal Maneuvers Around Binary Asteroids Using Particle Swarm Optimization and Machine Learning

Designing optimal transfer trajectories and reference orbit tracking in binary asteroid systems is both challenging and computationally expensive. This paper proposes a method of bypassing the high computational overhead by leveraging a collection of known techniques. Indeed, the proposed framework is based on the combination of artificial intelligence techniques, such as the particle swarm optimization and neural networks, along with the inverse dynamics and the B-splines approximation of the trajectory. The real irregular shapes of the asteroids are considered in the free dynamics of the system, which are obtained via the mutual polyhedral model. The gravitational accelerations of the single asteroids acting on the spacecraft are approximated by using two single-layer neural networks trained via an extreme learning machine. By using a combination of these techniques, the computational time of the whole optimization is decreased from hours to minutes. The proposed approach is applied to the optimal trajectory design around the binary asteroid system, 1999 KW4, showing the feasibility of the proposed optimization approach, reducing the computational effort and time, and increasing the reliability of the obtained results. It is shown through a Monte Carlo analysis that our optimization strategy yields more accurate solutions than other optimization algorithms, such as the interior point and sequential quadratic programming methods, when a random initial guess is provided. Finally, the proposed optimization approach can be used in combination with other techniques to provide a feasible and reliable initial guess for a better solution refinement.t

Technology Focus: Intelligent Operations (May 2023)

The first paper selected for this Intelligent Fields Technical Focus presents the use of a unique artificial-intelligence (AI) technique from the fourth industrial revolution—dual heuristic dynamic programming—to address business opportunities associated with optimization. The background, methodology, and novelty presented by the authors are clear; however, they need to benchmark their efforts and claims of improvement with peers. The second paper demonstrates well-structured content, though the topic is detailed, with the introduction of a digital laboratory called Ibtikar toward the enablement of connecting to open-source tools with the use of AI. The collaboration in research and development, as well as addressing challenges of upstream oil and gas requirements, is commendable. The authors, however, do not address associated security concerns concerning the open-source aspect of the technology. The third paper presents a combination of technologies used to automate daily data acquisition and model updates from production databases and run a supervised machine-learning model to continuously calibrate the network model. Recommended additional reading at OnePetro: www.onepetro.org. SPE 207188 Design and Development of a Solar-Powered Pump System With Liquid-Level Sensor and Controller Using Internet of Things Technology by Chinonyelum Ejimuda, Afe Babalola University, et al. SPE 211314 Improved Field Production Planning and Cost Optimization Using Predictive Analytical Choke Performance Model and Intelligent Alarms in an Integrated Platform by Ayesha Alsaeedi, Adnoc, et al. SPE 211367 Multidisciplinary Data Integration for Artificial-Intelligence-Assisted Well Placement and Trajectory-Design Optimization Under Uncertainty by Shi Su, SLB, et al.

UAV-Aided Dual-User Wireless Power Transfer: 3D Trajectory Design and Energy Optimization.

Unmanned aerial vehicles (UAVs) have been widely considered to enhance the communication coverage, as well as the wireless power transfer (WPT) of energy-constrained communication networks to prolong their lifetime. However, the trajectory design of a UAV in such a system remains a key problem, especially considering the three-dimensional (3D) feature of the UAV. To address this issue, a UAV-assisted dual-user WPT system was investigated in this paper, where a UAV-mounted energy transmitter (ET) flies in the air to broadcast wireless energy to charge the energy receivers (ERs) on the ground. By optimizing the UAV's 3D trajectory toward a balanced tradeoff between energy consumption and WPT performance, the energy harvested by all ERs during a given mission period was maximized. The above goal was achieved through the following detailed designs. On the one hand, on the basis of previous research results, there is a one-to-one correspondence between the UAV's abscissa and height, so only the relationship between the height and time was focused on in this work to obtain the UAV's optimal 3D trajectory. On the other hand, the idea of calculus was employed to calculate the total harvested energy, leading to the proposed high-efficiency trajectory design. Finally, the simulation results demonstrated that this contribution is capable of enhancing the energy supply by carefully designing the 3D trajectory of the UAV, compared to its conventional counterpart. In general, the above-mentioned contribution could be a promising way for UAV-aided WPT in the future Internet of Things (IoT) and wireless sensor networks (WSNs).

UAV Trajectory Design and Power Optimization for Terahertz Band-Integrated Sensing and Communications

Sixth generation (6G) wireless networks require very low latency and an ultra-high data rate, which have become the main challenges for future wireless communications. To effectively balance the requirements of 6G and the extreme shortage of capacity within the existing wireless networks, sensing-assisted communications in the terahertz (THz) band with unmanned aerial vehicles (UAVs) is proposed. In this scenario, the THz-UAV acts as an aerial base station to provide information on users and sensing signals and detect the THz channel to assist UAV communication. However, communication and sensing signals that use the same resources can cause interference with each other. Therefore, we research a cooperative method of co-existence between sensing and communication signals in the same frequency and time allocation to reduce the interference. We then formulate an optimization problem to minimize the total delay by jointly optimizing the UAV trajectory, frequency association, and transmission power of each user. The resulting problem is a non-convex and mixed integer optimization problem, which is challenging to solve. By resorting to the Lagrange multiplier and proximal policy optimization (PPO) method, we propose an overall alternating optimization algorithm to solve this problem in an iterative way. Specifically, given the UAV location and frequency, the sub-problem of the sensing and communication transmission powers is transformed into a convex problem, which is solved by the Lagrange multiplier method. Second, in each iteration, for given sensing and communication transmission powers, we relax the discrete variable to a continuous variable and use the PPO algorithm to tackle the sub-problem of joint optimization of the UAV location and frequency. The results show that the proposed algorithm reduces the delay and improves the transmission rate when compared with the conventional greedy algorithm.