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Due to its ability for flexible placement, the Unmanned Aerial Vehicle (UAV) has been widely utilized as an aerial relay to transmit the video streaming data, which is particularly useful in emergency communication networks that lack the communication infrastructure. In this paper, we examine the combined resource optimization and trajectory planning for video services using dynamic adaptive streaming over HTTP (DASH) in the uplink transmission of UAV-based emergency indoor-outdoor communication networks. In detail, a rotary-wing UAV works as an aerial relay, forwarding emergency video collected by indoor users to a remote Base Station (BS). To ensure that the UAV can forward the emergency video data from disaster area with stable video quality, we formulate an optimization problem of maximizing the uplink throughput and video streaming utility for all indoor users, by jointly optimizing the 3D flight trajectory of UAV, playback rate of video, communication time as well as bandwidth allocation, subject to the UAV trajectory constraints, total available bandwidth limitation, video quality variation constraints, as well as information-causality constraints for both UAV relaying and video playing. To tackle the formulated non-convex problem, we present a joint UAV trajectory, bandwidth allocation and video utility (JTBU) algorithm. Specifically, we first decouple the original problem into two sub-issues: 3D UAV trajectory optimization subproblem and resource allocation optimization subproblem. Then the JTBU algorithm alternately optimizes the two subproblems until the convergency is reached. Finally, the numerical results confirm that the proposed algorithm had a higher video streaming utility and system throughput than the baseline methods. [ABSTRACT FROM AUTHOR]

The paper is devoted to trajectory optimization for an inertial object moving in a plane with thrust bounded in magnitude in the presence of external forces. The aim is to maximize the longitudinal terminal velocity with the state constraint satisfied at each time to avoid a lateral collision with an obstacle. The linear tangent law is used as the basis for an algorithm that controls the direction of the thrust. Conditions for the existence of a solution are studied. Constraints on the initial lateral velocity and the time of the motion of the object are obtained. Since the linear tangent law violates the constraint for some motion times, a modified control law is proposed. A transcendental equation is obtained to find a critical value of time above which an undesired collision occurs. The corresponding conjecture is formulated, which allows us to eliminate the ambiguity that arises during the solution process. A method for solving the problem is presented and confirmed by numerical calculations. [ABSTRACT FROM AUTHOR]

Terahertz (THz) wireless communication is a promising technology that will enable ultra-high data rates, and very low latency for future wireless communications. Intelligent Reconfigurable Surfaces (IRS) aiding Unmanned Aerial Vehicle (UAV) are two essential technologies that play a pivotal role in balancing the demands of Sixth-Generation (6G) wireless networks. In practical scenarios, mission completion time and energy consumption serve as crucial benchmarks for assessing the efficiency of UAV-IRS enabled THz communication. Achieving swift mission completion requires UAV-IRS to fly at maximum speed above the ground users it serves. However, this results in higher energy consumption. To address the challenge, this paper studies UAV-IRS trajectory planning problems in THz networks. The problem is formulated as an optimization problem aiming to minimize UAVs-IRS mission completion time by optimizing the UAV-IRS trajectory, considering the energy consumption constraint for UAVs-IRS. The proposed optimization algorithm, with low complexity, is well-suited for applications in THz communication networks. This problem is a non-convex, optimization problem that is NP-hard and presents challenges for conventional optimization techniques. To overcome this, we proposed a Deep Q-Network (DQN) reinforcement learning algorithm to enhance performance. Simulation results show that our proposed algorithm achieves performance compared to benchmark schemes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are considered as a promising method to provide service for Internet of Remote Things (IoRT). The incorporation of non-orthogonal multiple access (NOMA) into UAV-assisted networks provides a possibility for massive IoRT devices access. In this paper, we investigate the downlink transmission UAV-assisted wireless communication system based on NOMA in IoRT networks, where UAVs fly vertically to provide services for IoRT devices. In order to transmit as much data as possible, we aim to maximize the energy efficiency (EE) of the UAV by jointly optimizing NOMA user scheduling, UAV trajectory and power allocation. Due to the coupling between variables, the derived problem is mixed integer optimization and non-convex. To solve this challenging problem, we first decouple the variables and split the original problem into three subproblems, i.e., user scheduling, trajectory planning, and power allocation. Then, a double-loop iterative algorithm is proposed to solve the corresponding subproblems based on the Dinkelbach method and successive convex approximation (SCA) techniques. Finally, simulation results give some insights e.g., NOMA outperforms OMA in both spectral efficiency and energy efficiency and the proposed NOMA-based EE-oriented scheme is approximate three times energy efficient than benchmark schemes. [ABSTRACT FROM AUTHOR]

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution. [ABSTRACT FROM AUTHOR]

With small size, low cost and high mobility, unmanned aerial vehicles (UAVs) are playing an increasingly important role in all areas of modern society. In this paper, a full-duplex (FD) UAV equipped with a large antenna array is investigated as a relay base station (BS) to provide services to multiple ground users (GUs) using millimeter-wave (mmWave). Specifically, we propose a joint optimization problem based on user scheduling, UAV flight trajectory, beamforming and UAV transmit power to maximize the average transmission rate of the overall system. As the problem described is highly coupled and non-convex, making it difficult to solve, we propose an iterative algorithm based on the block coordinate descent (BCD) technique and the successive convex approximation (SCA) techniques to obtain an approximate optimal solution. Simulation results show that the proposed algorithm is able to converge with a small number of iterations and can significantly improve the system throughput. [ABSTRACT FROM AUTHOR]

Path loss is important for the unmanned aerial vehicle (UAV) placement, trajectory optimization, and power allocation in UAV-aided communications. By considering both the factors of path delay and reflection angle (RA) in the non-line-of-sight (NLoS) paths, a new machine learning-based delay–angle-joint path loss prediction method for UAV mmWave channels is proposed. The one-input back propagation based neural network (BPNN) and two-input BPNN are built to predict the path power for line-of-sight (LoS) case and NLoS case, respectively. Meanwhile, a data acquisition method is developed to obtain massive data set for training the BPNN. According to the geometric information of digital map, a calculation method for path delay and RA is also proposed to drive the BPNN. The proposed method is simulated and analyzed based on ray-tracing (RT) simulated data under a typical urban scenario at 28 GHz. The 2D relationship of power–delay for the LoS case and 3D relationship of power–delay–RA for the NLoS case are obtained through the trained BPNN, which are well consistent with the validation set of RT data and outperform the traditional methods, i.e., 3GPP model and exponential model. Moreover, it's found that the path power rapidly decreases when RA is 65 ∘ – 75 ∘ under the simulation scenario, which could be an important reference for transceiver placement and route planning to reduce the impact of NLoS paths in the UAV channel. [ABSTRACT FROM AUTHOR]

Due to the superiority of large coverage, high mobility, high-quality channels, and reusability, high altitude platforms (HAPs) can be exploited as the aerial communication means to provide users in disaster areas with long-term and stable data relay services. In this correspondence, a HAP relaying system based on clustered non-orthogonal multiple access (C-NOMA) is designed in which the HAP provides data transfer services for base station (BS) and users who cannot communicate directly. According to the system, a joint HAP flight trajectory optimization and power allocation scheme is proposed, which aims at maximizing the downlink average achievable sum rate of all users while satisfying the information causality. Since the formulated problem is non-convex, we propose an efficient iterative algorithm to derive the approximately optimal solution by alternately optimizing the HAP flight trajectory, and the transmit power of BS and HAP. The numerical simulation results demonstrate that the proposed scheme can not only effectively improve the achievable sum rate, but also significantly enhance the fairness between users compared with the benchmark scheme. [ABSTRACT FROM AUTHOR]

In this work, a discrete maximum principle in Hamel's formalism for optimal nonholonomic motion planning is proposed, which is a discrete analogue of the usual necessary conditions for optimality obtained from the Pontryagin maximum principle. The exact Hamel integrator associated with discrete Lagrangian mechanics is adopted to derive the forced and nonholonomic integrator. A universal discrete nonholonomic optimal control framework based on moving frames is established. The optimal nonholonomic trajectory optimization for a wall-crawling mobile robot moving on a spherical tank is considered for the established framework, where the configuration space is a non-Euclidean space. The simulated results by the proposed framework accurately capture some interesting nonholonomic behaviors and geometric structures for the given mechanical model, and the feasibility and computing efficiency are verified by comparison with the open-loop control and direct parameter optimization methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have become the focus of current research because of their practicability in various scenarios. However, current local path planning methods often result in trajectories with numerous sharp or inflection points, which are not ideal for smooth UAV flight. This paper introduces a UAV path planning approach based on distance gradients. The key improvements include generating collision-free paths using collision information from initial trajectories and obstacles. Then, collision-free paths are subsequently optimized using distance gradient information. Additionally, a trajectory time adjustment method is proposed to ensure the feasibility and safety of the trajectory while prioritizing smoothness. The Limited-memory BFGS algorithm is employed to efficiently solve optimal local paths, with the ability to quickly restart the trajectory optimization program. The effectiveness of the proposed method is validated in the Robot Operating System simulation environment, demonstrating its ability to meet trajectory planning requirements for UAVs in complex unknown environments with high dynamics. Moreover, it surpasses traditional UAV trajectory planning methods in terms of solution speed, trajectory length, and data volume. [ABSTRACT FROM AUTHOR]

In this paper, an improved retractable body model (IRBM) is established, which has an advantage in simulating the flexion-and-extension motion of skier's legs during carved turning and straight gliding. The trajectory optimization problem for the nonlinear alpine skiing system is transformed into a multi-phase optimal control (MPOC) problem. Subsequently, a constrained multi-phase trajectory optimization model is developed based on the optimal control theory, where the optimization target is to minimize the total skiing time. The optimization model is discretized by using the Radau pseudospectral method (RPM), which transcribes the MPOC problem into a nonlinear programming (NLP) problem that is then solved by SNOPT solver. Through numerical simulations, the optimization results under different constraints are obtained using MATLAB. The variation characteristics of the variables and trajectories are analyzed, and four influencing factors related to the skiing time are investigated by comparative experiments. It turns out that the small turning radius can reduce the total skiing time, the flexion-and-extension motion of legs is beneficial to skier's performance, and the large inclination angle can shorten skier's turning time, while the control force has a slight effect on the skiing time. The effectiveness and feasibility of the proposed models and trajectory optimization strategies are validated by simulation and experiment results. [ABSTRACT FROM AUTHOR]

Within the emerging age of lunar exploration, optimizing transfer trajectories is a fundamental measure toward achieving more economical and efficient lunar missions. This study addresses the possibility of reducing the fuel cost of two-impulse Earth–Moon transfers in a four-body model with the Earth, the Moon, and the Sun as primaries. Lawden's primer vector theory is applied within this framework to derive a set of necessary conditions for a fuel-optimal trajectory. These conditions are used to identify which trajectories from an existing database could benefit from the insertion of an additional intermediate impulse. More than 10,000 three-impulse transfers are computed with a direct numerical optimization method. These solutions contribute to enriching the database of impulsive trajectories, useful to perform trade-off analyses. While the majority of trajectories exhibit only marginal improvements, a significant breakthrough emerges for transfers featuring an initial gravity assist at the Moon. Implementing a corrective maneuver after the lunar encounter yields substantial reductions in fuel costs. [ABSTRACT FROM AUTHOR]

Models of aircraft trajectories become important components of systems supporting the trajectory based operations paradigm: trajectory predictability is considered to be the main driver to enhance operational key performance areas, such as capacity of the airspace, effectiveness regarding all stakeholders' objectives, and, of course, safety. This article formulates the trajectory modelling problem as a data-driven imitation learning problem addressing multi-modality. To solve this problem we study the use of state-of-the-art multi-modal imitation learning methods Info-GAIL and Triple-GAIL operating in a supervised way, with the aim of (a) disentangling modalities representing patterns of trajectory evolution, and (b) predicting trajectories. Experiments are performed using a real-world dataset of long flights with origin Paris and destination Istanbul. Results show the potential of imitation learning methods to disentangle multi-modal trajectories in real-world settings and predict trajectories with high accuracy. [ABSTRACT FROM AUTHOR]

Supernumerary robotic limbs (SRLs) have great potentials to assist human in daily activities and industrial manufacturing by providing extra limbs. However, current SRLs have heavy and rigid structures that may threaten the operator safety; moreover, their limited degrees of freedom and movement modes are not suitable for complicated tasks. Although soft SRLs have exhibited advantages in structure compliance and flexible manipulation to address these problems, it remains challenging to accurately design the geometrical parameters to adapt to specific tasks, and accurate control is also required to realize the expected movement. Inspired by the biological characteristics of the octopus arm muscle fibers, fiber-reinforced actuators (FRAs) are employed to realize various motions, including extension, expansion, bending, and twisting; multiple FRAs are assembled to implement the SRL to achieve complex movement trajectories. The analytic model of the FRA is established to reveal the relationship between its deformation and geometrical parameters as well as input air pressures, which is validated with finite element simulation. Trajectory and payload optimization algorithms are proposed to optimally design the SRL and its control strategy with meeting the prescribed requirement of movement trajectory and payload capacity. Finally, experiments are conducted to validate the proposed robotic system. [ABSTRACT FROM AUTHOR]

The infiltration of heterogenous fleets of autonomous Unmanned Aerial Vehicles (UAVs) in smart cities is leading to the consumerization of city air space which includes infrastructure creation of roads, traffic design, capacity estimation, and trajectory optimization. This study proposes a novel autonomous Advanced Aerial Mobility (AAM) logistical system for high density city centers. First, we propose a real-time 3D geospatial mining framework for LiDAR data to create a dynamically updated digital twin model. This enables the identification of viable airspace volumes in densely populated 3D environments based on the airspace policy/regulations. Second, we propose a robust city airspace dynamic 4D discretization method (Skyroutes) for autonomous UAVs to incorporate the underlying real-time constraints coupled with externalities, legal, and optimal UAV operation based on kinematics. An hourly trip generation model was applied to create 1138 trips in two scenarios comparing the cartesian discretization to our proposed algorithm. The results show that the AAM enables a precise airspace capacity/cost estimation, due to its detailed 3D generation capabilities. The AAM increased the airspace capacity by up to 10%, the generated UAV trajectories are 50% more energy efficient, and significantly safer. [ABSTRACT FROM AUTHOR]

This study presents a novel framework that integrates the universal jamming gripper (UG) with unmanned aerial vehicles (UAVs) to enable automated grasping with no human operator in the loop. Grounded in the principles of granular jamming, the UG exhibits remarkable adaptability and proficiency, navigating the complexities of soft aerial grasping with enhanced robustness and versatility. Central to this integration is a uniquely formulated constrained trajectory optimization using model predictive control, coupled with a robust force control strategy, increasing the level of automation and operational reliability in aerial grasping. This control structure, while simple, is a powerful tool for various applications, ranging from material handling to disaster response, and marks an advancement toward genuine autonomy in aerial manipulation tasks. The key contribution of this research is the combination of a UG with a suitable control strategy, that can be kept relatively straightforward thanks to the mechanical intelligence built into the UG. The algorithm is validated through numerical simulations and virtual experiments. [ABSTRACT FROM AUTHOR]

Horizontal wells have significant advantages in coal bed methane exploration and development blocks. However, its application in new exploration and development blocks could be challenging. Limited geological data, uncertain geological conditions, and the emergence of micro-faults in pre-drilled target coal seams make it hard to accurately control the well trajectory. The well trajectory prior to drilling needs to be optimized to ensure that the drilling trajectory is within the target coal seam and to prevent any reduction in drilling ratio (defined here as the percentage of the drilling trajectory in the entire horizontal section of the well located in the target coal seam) caused by faults. In this study, the well trajectory optimization is achieved by implementing the following process to drill pilot hole, acquire 2D resonance, and azimuthal gamma logging while drilling. The pilot hole drilling can obtain the characteristic parameters of the target coal seam and the top and bottom rock layers in advance, which can provide judgment values for the landing site design and real-time monitoring of whether the wellbore trajectory extends along the target coal seam; 2D resonance exploration can obtain the construction of set orientation before drilling and the development of small faults and formation fluctuations in the horizontal section, which can optimize the well trajectory in advance; the azimuth gamma logging while drilling technology can monitor the layers drilled by the current drill bit in real time, and can provide timely and accurate well trajectory adjustment methods.The horizontal well-Q in the Block-W of the Qinshui Basin was taken as a case study and underwent technical mechanism research and applicability analysis. The implementation of this new innovative process resulted in a successful drilling of a 711 m horizontal section, with a target coal seam drilling rate of 80%. Compared to previous L-type wells, the drilling rate increased by about 20%, and the drilling cycle shortened by 25%. The technical experience gained from this successful case provides valuable insight for low-cost exploration and development of new coalbed methane blocks. [ABSTRACT FROM AUTHOR]

Biodiesel stands out as a promising contender in the quest for renewable energy solutions, offering a greener alternative to traditional fossil fuels. Derived primarily from the transesterification of vegetable oils or animal fats, biodiesel offers an eco-friendly energy avenue with a minimized carbon footprint. Catalysts are central to the success of this process, which significantly enhance yield rates. Geopolymers, traditionally associated with construction applications due to their inorganic nature, have been derived from aluminosilicate sources activated using alkaline solutions. However, recent advancements spotlight geopolymers in a new light, emphasizing their prospective role as nanocatalytic agents for biodiesel synthesis. This paradigm shift suggests improved production efficiency and an innovative method of repurposing industrial waste. This study centers on the pioneering application of geopolymers, fortified with magnetite, as potent heterogeneous catalysts for biodiesel generation from soybean and safflower oils. By leveraging a meticulously crafted geopolymer matrix—consisting of metakaolin, sodium hydroxide, and magnetite—this research replaced traditional catalysts with this advanced nanostructured geopolymer variant in the biodiesel methylation process. The research delved deep to ascertain the prime synthesis conditions. Furthermore, utilizing cutting-edge machine learning methodologies provided an analytical lens to navigate the extensive experimental data, thereby fine-tuning the optimization trajectory. One of the salient takeaways from this research is the validation that geopolymer catalysts, rooted in kaolinite, can be ingeniously tailored to ensure elevated biodiesel yields across a spectrum of oil sources, underscoring their unparalleled efficiency and versatility in the biofuel domain. [ABSTRACT FROM AUTHOR]

With the resumption of the World Cup and various concerts, the number of heavily crowded scenarios grows intensely. In these cases, it poses new challenges to massive information collection and lightweight target detection. Fortunately, the booming development of unmanned aerial vehicle (UAV) technology provides a highly flexible and cost-effective solution in many scenarios. This paper proposes a UAV aided lightweight target information collection and detection approach, where the target information is carried to a terrestrial distributed platform by a UAV and then a fast target detection is implemented. Firstly, we implement a 3D trajectory optimization for the UAV by minimizing the information collection time. Secondly, we design a lightweight target detection algorithm based on UAV loadable ARM (Advanced RISC Machines) architecture edge computing device. Finally, a terrestrial distributed processing platform is established. To ensure the stability and reliability of the target detection system, each module is tested separately. Numerical simulations show that, with the target detection module deployed on the Jetson Xavier NX edge computing platform for testing, the proposed target detection approach can achieve a detection accuracy of 89.5 % and 71FPS detection speed using GPU acceleration compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the progress of SpaceX's Starship system, a fully reusable launch system appears possible in the near future. With this technological advance, a new class of transport missions could become economically feasible: Rocket-propelled transport of passengers and cargo at near-orbital speeds from one point on the Earth to another. This mission type is investigated for two reference vehicles: Starship from SpaceX and the SpaceLiner concept, developed by DLR-SART. For both vehicles, the properties during reentry are assessed and the impact on the descent trajectory is investigated, including parametric studies with regard to the effect of launch heading and crossrange capability. While the SpaceLiner upper stage relies on its high hypersonic L/D to cover the majority of the distance in quasi-stationary flight within the atmosphere, the Starship relies mostly on a ballistic flight path at higher altitudes. The flight within the upper layers of the atmosphere allows the SpaceLiner to change its heading mid-flight through banking maneuvers and thus efficiently curve around populated areas. However, this prolonged flight at high velocities within the upper atmosphere does necessitate active cooling of the leading edges. An exemplary mission from Shanghai to California is optimized for both reference vehicles, with the optimization target being minimal peak heat loads. Due to the Starship's reliance on the ballistic portion of its flight to cover range, the peak heat flux is substantially higher on westward missions, compared to the eastward return flight. [ABSTRACT FROM AUTHOR]

The irregular satellites within the Jupiter system hold high scientific value due to their potential to contain clues about the early evolution of the solar system. This paper proposes an optimization algorithm for multiple irregular satellites flyby trajectories, which includes the powered gravity assist from Galilean moons. The algorithm is based on beam search and uses virtual trajectories to determine potential flyby targets, solving for trajectories that satisfy constraints on velocity increment, mission duration, and perijove radius. By changing the initial orbital period and optimizing the number of branches with Galilean moon gravity assist during branching, and comparing with the case without Galilean moon gravity assist, the effects of these factors on the number of irregular satellite flybys are summarized. The simulation results show that the algorithm can effectively solve for orbits that flyby multiple irregular satellites for initial orbits of different periods. The three trajectories obtained can serve as references for future missions. [ABSTRACT FROM AUTHOR]

Purpose: This paper proposes a novel dual four-rod horizontal large-amplitude quasi-zero stiffness (QZS) vibration isolator based on the singular configuration of a planar four-rod mechanism combined with gravity compensation. Methods: First, the mechanism design and three-dimensional model of dual four-rod horizontal vibration isolator are established. Second, kinematic characteristics are analyzed, and trajectory planning is carried out to obtain the kinematic performance. Then, based on the artificial fish swarm algorithm, the appropriate size and mass of the component are solved, and the QZS characteristics of the isolator are optimized. Third, the statics and dynamics theoretical models of isolators are established, and the transmissibility of isolators under different damping ratios and excitation amplitudes is solved by simulation experiments. Finally, the experimental prototype is established, and the experiment of resilience and acceleration transmissibility is carried out to verify the effectiveness of low-frequency vibration isolation. Results: The static and dynamic simulation analysis of isolator well verifies the theoretical solution of isolator. According to the theoretical model, different vibration isolation performance can be obtained with different input parameters. The experimental results of the prototype show that the isolator has lower initial vibration isolation frequency and wider vibration isolation bandwidth. Conclusion: The dual four-rod horizontal large-amplitude QZS isolator designed in this paper shows good performance in isolating the external excitation of the low-frequency large vibration amplitude. Since the medical precision instrument will inevitably produce low-frequency vibration during the transportation process and reduce the accuracy and stability of the equipment, in order to reduce the possibility of the instrument being damaged during the transportation process, the vibration isolator has great application potential in the isolation of medical precision instrument. [ABSTRACT FROM AUTHOR]

This paper presents a new study on modeling and optimization of trajectory and posture for the super-giant (SG) slalom of alpine skiing. It is the first time that a Three-Rigid-Body-Particle model based on rigorous derivations and stability analysis is established to represent skiers trajectory and posture characteristics, as it is more accurate than the single-rigid-body model which is commonly used in existing studies. In addition, the Radau pseudospectral method is applied to solve the trajectory and posture optimization problem in order to obtain better skiing trajectory, skiing posture, and some key kinematic parameters of skiers. Moreover, this paper analyzes the effects of different body types, minimum turning radii, and flexor and extensor strength of knees and hip joint on skiing performance. Finally, based on the findings of the study, some advice about how to improve the performance of the SG slalom in view of science and technology is given to skiers and coaches for reference. [ABSTRACT FROM AUTHOR]

A method is proposed to generate an initial guess for impulsive trajectory design in the circular restricted three-body problem. The method uses acceleration-based switching surfaces to obtain near-impulsive solutions. A numerical continuation is performed on the maximum acceleration value to find near-impulsive solutions. A nonlinear programming problem is formulated by providing primer vector based analytical gradients. The solution space is narrowed down to aid the optimizer with the use of the near-impulsive solutions. The proposed method is used for the trajectory design of four different maneuvers between L1 and L2 Halo orbits in the Earth–Moon system. The results demonstrate the utility of the proposed method in generating extremal impulsive trajectories. [ABSTRACT FROM AUTHOR]

Solutions to nonlinear optimal control problems (OCPs) exhibit dispersions under model uncertainties and it is desirable to generate optimal solutions that exhibit less sensitivity to model uncertainties. We propose a novel solution desensitization method dubbed “Reduced Desensitization Formulation (RDF)” by leveraging non-uniqueness of the solution of the costate differential equations when a hybrid indirect-direct optimization method is used. A key property of the RDF method is a significant reduction in the number of differential equations needed for generating desensitized solutions. This feature facilitates the generation of open-loop desensitized trajectories and makes the methodology applicable to OCPs with a larger number of uncertain parameters. To demonstrate the utility of the RDF method, three important classes of trajectory optimization problems are considered with uncertainty in the thrust magnitude of the propulsion system: (1) minimum-fuel low-thrust interplanetary rendezvous maneuvers, (2) low-thrust orbit-raising maneuvers, and (3) minimum-fuel high-thrust rocket-landing problems. For the considered problems with bang-bang control profiles, an analysis is presented on the change in the number of control switches between sensitive and desensitized optimal solutions. Numerical results demonstrate desensitization of the considered performance indices with respect to the thrust magnitude of the propulsion system. [ABSTRACT FROM AUTHOR]

Considering the nonlinearity and unknown dynamics of fixed-wing unmanned aerial vehicles in perched landing maneuvers, an event-based online guidance and incremental control scheme is proposed. The guidance trajectory for perched landing must be dynamically feasible therefore an event-based trapezoidal collocation point optimization method is proposed. Introduction of the triggering mechanism for the rational use of computing resources to improve PL accuracy. Furthermore, a filter-based incremental nonlinear dynamic inverse (F-INDI) control with state transformation is proposed to achieve robust trajectory tracking under high angle of attack (AOA). The F-INDI uses low-pass filters to obtain incremental dynamics of the system, which simplifies the design process. The state transformation strategy is to convert the flight-path angle, AOA and velocity into two composite dynamics, which avoids the sign reversal problem of control gain under high AOA. The stability analysis shows that the original states can be controlled only by controlling the composite state. Simulation results show that the proposed scheme achieves high perched landing accuracy and a reliable trajectory tracking control. [ABSTRACT FROM AUTHOR]

The ollie is the base aerial human–board maneuver, foundational to most modern skateboarding tricks. We formulate and solve an optimal control problem of a two-dimensional simplified human model and a rigid body skateboard with the objective of maximizing the height of the ollie. Our solution simultaneously discovers realistic human-applied force trajectories and optimal board geometry. We accomplish this with a direct collocation formulation using a null seed initial guess by carefully modeling the discontinuous aspects of board–ground impact and foot–board friction. This leads to efficient and robust solutions that are 10 times more computationally efficient than prior work on similar problems. The solutions show that ollie height can increase 3% by decreasing the wheelbase and that a smaller board with a back-foot-dominated force strategy can give 12% higher ollies. Our model can be used to inform jump strategy and the effects of changes to the essential board geometry. [ABSTRACT FROM AUTHOR]

Data processing is a key challenge for computationally limited Ground Users (GUs) in various applications. Unmanned Aerial Vehicles (UAVs) equipped with Multi-access Edge Computing (MEC) servers can assist GUs by offloading their computing tasks. However, existing work ignores fairness when multiple GUs compete for limited computing resources, which may result in UAV underserving certain GUs. In this paper, we investigate a flight trajectory optimization based on reinforcement learning for UAV selection of target GUs for task computation, which provides low latency and fair offloading computing services for GUs by jointly training UAV flight trajectories and task offloading decisions. We formulate UAV flight and offloading as a mixed integer non-convex optimization problem with high-dimensional state and action spaces. The problem is then transformed into a Markov Decision Processes (MDPs) problem and the Maximizing Service Efficiency Proximal Policy Optimization (MSE-PPO) algorithm is proposed to find the optimal solution. The algorithm adopts an actor-critic-based parallel architecture to handle the parameterized action space. Specifically, the UAV position sequence is updated while ensuring an optimal offloading policy between the UAV and the GUs. Simulation results verify that the average system rewards including computational energy efficiency and fairness index are improved by 35.06 % and 12.10 % respectively compared to DDPG and PPO algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs using the k-means clustering algorithm. Then, a low-complexity greedy mechanism is used to generate the order of HPs assigned to each UAV, determining its trajectory. Several experiments are conducted to assess the effectiveness of the MTPAs with variable population size and cyclic selection mechanisms. The experimental findings demonstrate that the MTPAs with the cyclic selection mechanism outperform all competing algorithms, achieving better outcomes. [ABSTRACT FROM AUTHOR]

Future missions to Enceladus would benefit from multi-moon tours that leverage V ∞ on resonant orbits to progressively transfer between moons. Such resonance hopping trajectories present a vast search space for global optimization due to the different combinations of available resonances and flyby velocities. The proposed multi-objective tour design algorithm optimizes entire moon tours from Titan to Enceladus via grid-based dynamic programming, in which the computation time is significantly reduced by discretization of the design variables and pre-computation of a database of V ∞ -leveraging transfers. The result unveils a complete trade space of the moon tour design to Enceladus, and the obtained solution is validated in a full-ephemeris model. [ABSTRACT FROM AUTHOR]

The problem of effectively controlling the traction of an all-wheel drive wheeled robot after its sharp turn caused by the sudden appearance of an extended obstacle in the robot path has been solved. It is assumed that, in the course of steering, the robot body is parallel to the obstacle and its front wheels are aligned. The task is to ensure acceleration along the obstacle, while avoiding a side collision with it. The solution is based on the linear tangent law adapted to phase restrictions. On a finite time interval, the speed of wheel rotation is obtained in the course of lateral motion in the drift mode and the subsequent acceleration on the verge of slipping along a straight line that is as close as possible to the boundary of the obstacle. The corresponding trajectory is also shown. The dependence of the longitudinal speed developed at the end of the maneuver on the initial distance to the obstacle and the maneuver time is studied. The left-side limits of the wheel angular acceleration and the power at the end of the sliding segment are determined. The found trajectory is compared with some other trajectories consisting of a curved and a straight segment. Numerical calculations show that the former trajectory is more effective. [ABSTRACT FROM AUTHOR]

Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined a priori with the ability to shift the trajectory of the optimization based on human-identified findings during the experiment is lacking. Thus, to highlight the best of both human operators and AI-driven experiments, here we present the development of a human–AI collaborated experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly with human real-time feedback. Here, the human guidance overpowers AI at early iteration when prior knowledge (uncertainty) is minimal (higher), while the AI overpowers the human during later iterations to accelerate the process with the human-assessed goal. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and in real-time on an atomic force microscope, with human assessment to find symmetric hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human–AI approaches for curiosity driven exploration of systems across experimental domains. [ABSTRACT FROM AUTHOR]

The solar system boundary exploration mission has the characteristics of long flight time, high fuel consumption, complex gravity-assist sequence and strict constraints. Therefore, the number of decision variables and the search space of the transfer trajectory are very large, resulting in poor convergence and efficiency of the global search of the metaheuristic algorithm. Moreover, the existing gravity assist space pruning algorithm is no longer applicable for solar system boundary exploration. To effectively reduce the search space and improve the effect of trajectory optimization, an improved gravity assist space pruning algorithm is proposed. In this algorithm, a unique pruning procedure is used to effectively prune the search space, a shape of solution space box bounds combining rectangle and rhombus is adopted, and a method to automatically determine the solution space box bounds is presented. To verify the effectiveness of the improved gravity assist space pruning algorithm, the sensitivity of pruning effect to parameters is analyzed and the optimization effects of three typical metaheuristics are compared. The optimization results of 50 repeated runs of the differential evolution algorithm in the entire search space and the solution space box bounds are compared. Simulation results show that the performance of differential evolution algorithm is better than bat algorithm and firefly algorithm. And the improved pruning algorithm can increase the efficiency of subsequent optimization by more than eleven times and the convergence probability of the objective function by fifty of times. The applicability and efficiency of the proposed method for the solar system boundary exploration are demonstrated. [ABSTRACT FROM AUTHOR]

This paper proposes a receding horizon based motion planning method, which allows a sensory-constrained quadrotor to dynamically plan obstacle-avoiding trajectories in unknown complex environments. First, a two-process search method is proposed to generate an initial feasible path satisfying the dynamics of the quadrotor. Second, the path smoothness is improved by solving a nonlinear optimization problem considering path safety and smoothness. Then, a uniform B-spline is used to interpolate the path with a receding horizon to achieve a safe and dynamically feasible trajectory with minimum trajectory time by solving an optimization problem. Finally, a time adjustment method is proposed based on the relationship between the distance of the B-spline trajectory and the obstacles. Extensive simulation results illustrate that the designed method doubles the safety range, defined as the minimum distance between the quadrotor and the obstacles, and consumes less than 70% of computational running time compared with the state-of-the-art. Outdoor flight experiments performed with a vision-based quadrotor show the satisfying performance of the motion planning approach. [ABSTRACT FROM AUTHOR]

Optimal control problems (OCPs) with a control-affine Hamiltonian may lead to extremal solutions with singular control arcs. The presence of singular control arcs along with multiple nonlinear state path inequality constraints introduces numerical complications during the solution process. Leveraging the primer vector theory of Lawden, we propose an alternative derivation of extremal control expressions for solving OCPs with mixed regular and singular control arcs and in the presence of multiple state path inequality constraints. As an application, we consider the classical Goddard rocket vertical ascent problem with bounded thrust control and two state path inequality constraints on the magnitude of the dynamic pressure and velocity. In addition, an analysis of the switching function time history for OCPs with singular control arcs and state path constraints is presented. The results demonstrate that the proposed method offers noticeable implementation simplicity and provides more accurate solutions with respect to approximating singular control arcs and in capturing the switch instants between different control arcs when compared against a direct optimization method. [ABSTRACT FROM AUTHOR]

It is often unnoticed that the predominant way to use collocation methods is fundamentally flawed when applied to optimal control in robotics. Such methods assume that the system dynamics is given by a first order ODE, whereas robots are often governed by a second or higher order ODE involving configuration variables and their time derivatives. To apply a collocation method, therefore, the usual practice is to resort to the well known procedure of casting an Mth order ODE into M first order ones. This manipulation, which in the continuous domain is perfectly valid, leads to inconsistencies when the problem is discretized. Since the configuration variables and their time derivatives are approximated with polynomials of the same degree, their differential dependencies cannot be fulfilled, and the actual dynamics is not satisfied, not even at the collocation points. This paper draws attention to this problem, and develops improved versions of the trapezoidal and Hermite–Simpson collocation methods that do not present these inconsistencies. In many cases, the new methods reduce the dynamics transcription error in one order of magnitude, or even more, without noticeably increasing the cost of computing the solutions. [ABSTRACT FROM AUTHOR]

In this paper, we concentrate on a non-orthogonal multiple access (NOMA)-enabled UAV data collection network for Internet of Things devices (IoTDs), where a unmanned aerial vehicle (UAV) is deployed as an aerial base station. During its flight period, the UAV can collect data from IoTDs and take advantage of the simultaneous wireless information and power transfer technology to charge the batteries of IoTDs. With the aid of NOMA, spectrum efficiency has been improved. We aim to prolong the lifetime of the IoT network, via jointly optimizing the UAV trajectory, the time allocation for information communication and wireless power transfer, the IoTDs' transmit power, as well as the IoTDs' group scheduling for NOMA. Then, we use the block coordinate decent and successive convex approximation techniques to tackle the non-convexity of the formulated problem. Numerical results show that the proposed solution increases the residual energy of the IoTDs, thus prolonging the lifetime of the network. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel hybrid heuristic algorithm, particle swarm optimization, and whale optimization algorithm (PSO–WOA), to solve a multi-objective optimization problem relating to point-to-point trajectory planning of space robots. First of all, the kinematics of the space robot is introduced, and the motion of each revolute joint of the manipulator is parameterized by Bézier curve. Then, contradictory objective functions are proposed, and the trajectory planning problem is transformed into a multi-objective optimization problem. The pose of the end-effector at the end of motion is set as the primary objective. The base disturbance, execution time, and manipulability of the end-effector are also taken into account. Furthermore, self-collision avoidance during the motion is also considered. The trajectory planning problem finally comes down to finding an optimal parameter of the Bézier curve for each joint. We propose a novel hybrid PSO–WOA, which is supposed to take advantages of the best of both methods: the exploration feature of the WOA and exploitation feature of the PSO. In order to enhance the performance of the PSO–WOA, the good point set and lévy flight stochastic steps are employed for the initialization and updating process, respectively. The proposed method is applied to generate an optimal trajectory for a redundant free-floating space robot. The simulation results demonstrate the effectiveness of the PSO–WOA. [ABSTRACT FROM AUTHOR]

In order to extend the efficiency of linear fractional order controllers for nonlinear and preview systems, this paper shows how to design an optimal, robust and safe control-system. The robustness of the control-system is achieved with a linear and fractional order feedback controller that takes into account model uncertainties and frequency-domain constraints. Complementary, a nonlinear model-based optimization is used to design a nominal anticipative feedforward control and reference closed-loop trajectory. A timescale separation between the optimal feedforward and the feedback loop allows reducing the sensitivity of the obtained performance to the required computing time, improving the safety of the overall control system. The proposed approach is evaluated using the Matlab Simulink nonlinear model of the intake manifold pressure of a spark ignition (SI) internal combustion engine, in which parametric uncertainties and measurement noise are considered. [ABSTRACT FROM AUTHOR]

Autonomous mining transportation is an intelligent traffic control system that can provide better economics than traditional transportation systems. The velocity trajectory of a manned vehicle depends on the driver's driving style. Still, it can be optimized utilizing mathematical methods under autonomous driving conditions. This paper takes fuel and electric mining vehicles with a load capacity of 50 tons as the subject. It contributes a multi-objective optimization approach considering time, energy consumption, and battery lifetime. The dynamic programming (DP) algorithm is used to solve the optimal velocity trajectory with different optimization objectives under two types of mining condition simulation. The trajectories optimized by the single objective, energy consumption, usually adopt the pulse-and-gliding (PnG) approach frequently, which causes the battery capacity loss and increases the travel time. Hence, a multi-objective optimization approach is proposed. For electric vehicles, trajectories optimized by the multi-objective approach can decrease the battery capacity loss by 22.01 % and the time consumption by 41.28 %, leading to a 42.12 % increment in energy consumption. For fuel vehicles, it can decrease the time consumption by 32.54 %, leading to a 7.68 % increment in energy consumption. This velocity trajectory is smoother with less fluctuation. It can better meet the requirements of mining transportation and has a particular reference value for optimizing autonomous transportation costs in closed areas. [ABSTRACT FROM AUTHOR]

In Jupiter missions, single-satellite-aided capture employs the gravity assist of one of the Galilean moons, which could decrease the required Δ v to capture a spacecraft into a nominal orbit around Jupiter. Previous studies on single-satellite-aided capture mainly focused on Jovicentric orbits. In this paper, an optimization that focuses on Jovicentric orbits and considers the planetary segment and the nominal orbit around Jupiter was proposed. We use Jupiter arrival excess velocity and arrival time as the link between these two segments, which clearly illustrates single-satellite-aided capturing. VEGA and VEEGA sequences (both planetary gravity assist sequences) are tested in the numerical simulation. The result, with the complete orbits of the mission, indicates the reliability and good convergence of the method. It also turns out that Io and Ganymede are the best options for satellite-aided capture. [ABSTRACT FROM AUTHOR]

The demand for mobile robotics applications has grown considerably in recent years, especially due to the advent of industry 4.0, which has as one of its pillars the autonomous robotics field, the subject of this research. In this context, autonomous mobile robots must interact with the world to achieve their goals. One of the main challenges regarding mobile robots is the navigation problem: a robot can face several problems according to the type of sensor that is chosen in each application. The use of computer vision as a navigation tool in robotics represents an interesting alternative for controlling the movement of a mobile robot, and represent several vision techniques that gained more space in the last few years. Therefore, this work's research proposes the development of a control center to assist navigation and location of mobile robots in closed environments using the global view technique. In addition to computer vision, wireless communication (WiFi) between the exchange and the robots has been investigated to date. The results obtained in the initial steps of the project's development were promising, in which data from an autonomous robot is compared with a human-guided robot. Through the algorithm developed for the project, it was possible to transform the collected data into the robot's kinematics necessary to take the correct path to the destination using multivalued logic as a control algorithm. The optimization of the trajectory between the origin and the destination is performed using the A* and Dijkstra algorithms for calculating the shortest path. [ABSTRACT FROM AUTHOR]

In this paper, we propose an unmanned aerial vehicle (UAV)-assisted wireless coverage scheme, wherein UAVs help to serve the cell-edge users for ground base stations based on non-orthogonal multiple access (NOMA). The combined benefits of wide coverage and massive connections enable UAV-NOMA-aided wireless coverage to effectively meet the requirements of the Internet of Everything. Meanwhile, energy efficiency (EE) is maximized via resource allocation to address the issue of the power supply bottleneck of UAVs. The non-convex optimization problem is transformed into two convex subproblems of trajectory optimization and power allocation, and an iterative alternating optimization algorithm is proposed to address it effectively. Moreover, we further considered the energy consumption of the ground base stations, effectively minimizing it by optimizing precoding while guaranteeing average throughput. Numerical results verify the effectiveness of the proposed UAV-aided wireless coverage scheme and demonstrate the EE improvement. [ABSTRACT FROM AUTHOR]

Nowadays, the space exploration is going in the direction of exploiting small platforms to get high scientific return at significantly lower costs. However, miniaturized spacecraft pose different challenges both from the technological and mission analysis point of view. While the former is in constant evolution due to the manufacturers, the latter is an open point, since it is still based on a traditional approach, not able to cope with the new platforms’ peculiarities. In this work, a revised preliminary mission analysis approach, merging the nominal trajectory optimization with a complete navigation assessment, is formulated in a general form and three main blocks composing it are identified. Then, the integrated approach is specialized for a cislunar test case scenario, represented by the transfer trajectory from a low lunar orbit to an halo orbit of the CubeSat LUMIO, and each block is modeled with mathematical means. Eventually, optimal solutions, minimizing the total costs, are sought, showing the benefits of an integrated approach. [ABSTRACT FROM AUTHOR]

Deployment of robotic systems in the real world requires a certain level of robustness in order to deal with uncertainty factors, such as mismatches in the dynamics model, noise in sensor readings, and communication delays. Some approaches tackle these issues reactively at the control stage. However, regardless of the controller, online motion execution can only be as robust as the system capabilities allow at any given state. This is why it is important to have good motion plans to begin with, where robustness is considered proactively. To this end, we propose a metric (derived from first principles) for representing robustness against external disturbances. We then use this metric within our trajectory optimization framework for solving complex loco-manipulation tasks. Through our experiments, we show that trajectories generated using our approach can resist a greater range of forces originating from any possible direction. By using our method, we can compute trajectories that solve tasks as effectively as before, with the added benefit of being able to counteract stronger disturbances in worst-case scenarios. [ABSTRACT FROM AUTHOR]

This article investigates a new autonomous mobile fog computing (MFC) system empowered by multiple unmanned aerial vehicles (UAVs) in order to serve medical Internet of Things devices (MIoTDs) efficiently. The aim of this article is to reduce the energy consumption of the UAVs-empowered MFC system by designing UAVs' trajectories. To construct the trajectories of UAVs, we need to consider not only the order of SPs but also the association among UAVs, SPs, and MIoTDs. The above-mentioned problem is very complicated and is difficult to be handled via applying traditional techniques, as it is NP-hard, nonlinear, non-convex, and mixed-integer. To handle this problem, we propose a novel simulated annealing trajectory optimization algorithm (SATOA), which handles the problem in three phases. First, the deployment (i.e., number and locations) of stop points (SPs) is updated and produced randomly using variable population sizes. Accordingly, MIoTDs are associated with SPs and extra SPs are removed. Finally, a novel simulated annealing algorithm is proposed to optimize UAVs' association with SPs as well as their trajectories. The performance of SATOA is demonstrated by performing various experiments on nine instances with 40 to 200 MIoTDs. The simulation results show that the proposed SATOA outperforms other compared state-of-the-art algorithms in terms of saving energy consumption. [ABSTRACT FROM AUTHOR]

This paper derives the dynamic equations of a reduced-order race-car model using Lie-group methods. While these methods are familiar to computational dynamicists and roboticists, their adoption in the vehicle dynamics community is limited. We address this gap by demonstrating how this framework integrates smoothly with the Articulated-Body Algorithm (ABA) and provides a fresh and systematic formulation of vehicle dynamics. For the first time, we model the car body as the end effector of a serial robot with a floating base connected to the track via virtual revolute and prismatic joints. Our formulation also accounts for the effects of 3D track geometry, providing a natural embedding of the car into the 3D track. We rigorously reconcile the ABA steps with key aspects of vehicle dynamics, including road-tire interactions, aerodynamic forces, and load transfers. The resulting model, simple yet accurate, is a powerful tool to efficiently solve Minimum-Lap-Time Planning problems. To demonstrate the effectiveness of our approach, we show numerical results obtained on the Nürburgring circuit. Our optimization problem is formulated via a direct collocation method and solved using the CasADi optimization suite. To validate the results, we test our reduced-order model against a full-fledged multi-body model recently developed by the same authors. The comparison confirms the validity of our reduced-order model, proving both the accuracy of the solution and the computational efficiency achieved. [ABSTRACT FROM AUTHOR]

With the inherent advantages of exceptional maneuverability, flexible deployment options and cost-effectiveness, unmanned aerial vehicles (UAVs) present themselves as a viable solution for providing aerial communication services to Internet of Things devices in high-traffic or remote areas. Nevertheless, the openness of the air–ground channel poses significant security challenges to UAV-based wireless systems. In this paper, a UAV-assisted secure communication system model is established based on non-orthogonal multiple access (NOMA) from the perspective of physical layer security, aiming to conceal the transmission behavior between UAVs and legitimate users (LUs). Specifically, a mobile UAV assumes the role of an aerial base station, leveraging NOMA technique to transmit data to LUs while evading detection from mobile eavesdropper situated on the ground. To fortify the security performance of the system, a hovering UAV acts as a friendly jammer and transmits interference signals to mobile eavesdropper (referred to as Eve). The objective of this scheme is to maximize the minimum average secure rate of all LUs by meticulously optimizing the trajectory and power allocation of the mobile UAV, subject to secrecy performance constraints. The highly interdependent and non-convex nature of this optimization problem renders direct solutions infeasible. Hence, this paper designs an efficient iterative algorithm that decouples the original problem into two subproblems, enabling an alternating optimization process for the trajectory and power allocation of the mobile UAV until the convergence of the objective function is achieved. Simulation results demonstrate that the proposed algorithm effectively improves the minimum average secure rate of all LUs compared with the benchmark scheme. [ABSTRACT FROM AUTHOR]

This paper investigates an optimization problem corresponding to energy efficiency maximization of an unmanned aerial vehicle (UAV)-enabled relaying system, where a fixed-wing UAV acts as an amplify-and-forward mobile relay to assist data transmission between a source node and a destination node. On the premise of satisfying the speed and acceleration constraints of the UAV, the energy efficiency (EE) of the relaying system is maximized by jointly optimizing the UAV's trajectory and the individual transmit power levels of the source and the UAV relay. The initial joint optimization problem is non-convex and cannot be solved directly. Therefore, the joint optimization problem is decomposed into two sub-problems which are solved by applying the successive convex approximation technique and the Dinkelbach's algorithm. On this basis, an efficient iterative algorithm is proposed to tackle the joint optimization problem through the block coordinate descent technique. Simulation results demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm contributes to the higher EE compared with the benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, the flexibility of unmanned aerial vehicles (UAVs), as well as the benefits of coordinated multi-point (CoMP) transmission, are utilized for mitigating the interference in cellular networks. Specifically, the joint problem of CoMP clusters and UAVs' trajectories is addressed for downlink transmission in a UAV-assisted cellular system. The problem is presented as a non-convex optimization problem that aims to maximize the sum rate of the ground users by taking into account the clustering, UAV mobility and backhaul capacity constraints. Since the formulated problem is known to be NP-hard, we partition it into two sub-problems. Particularly, by using coalitional game theory, the CoMP clusters are obtained with a given UAVs' trajectories. Then, UAVs' trajectories are optimized with given CoMP clusters using successive convex approximation technique. Based on the block coordinate descent method, the two sub-problems are solved alternatively until convergence. Numerical results are conducted and demonstrated the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]