Optimal Unmanned Aerial Vehicle Trajectory Research Articles

UAV-assisted wireless powered Internet of Things: Joint trajectory optimization and resource allocation

Due to affordable price, high mobility and flexible maneuverability, Unmanned Aerial Vehicle (UAV)-assisted communication can play an important role in the deployment of the Internet of Things (IoT) in emergency. Since UAV network performance is highly dependent on UAV deployment location, trajectory design becomes the research hotspot in UAV-assisted communication. To this end, a UAV-assisted multicarrier wireless powered communication model is proposed for IoT scenarios in this paper. Specifically, as an aerial base station, the UAV transmits Orthogonal Frequency Division Multiplexing (OFDM) signals to IoT nodes, while IoT nodes decode information and harvest energy from the signals. Afterwards, IoT nodes transmit information to the UAV by using the harvested energy. A joint UAV trajectory optimization and resource allocation scheme based on OFDM is proposed. The aim is to maximize the minimum achievable rate in the uplink among all IoT nodes by jointly optimizing UAV trajectory, subcarrier, power and subslot allocation, subject to the achievable sum rate of all IoT nodes in the downlink. Due to the non-convexity and complexity of the formulated optimization problem, an alternative iteration algorithm is proposed to deal with the problem. Simulation results that the proposed algorithm can optimize the UAV trajectory and adapt to the node movement. Compared with conventional resource allocation schemes, the proposed scheme not only significantly enhances the minimum achievable rate, but also works well for two flight modes.

Noncausal trajectory optimization for real-time range-only target localization by multiple UAVs

This study proposes a new method namely noncausal trajectory optimization which is developed to obtain superior performance in range-only (i.e. distance and received signal strength based) target localization by multiple unmanned aerial vehicles (UAVs). Noncausal trajectory optimization suggests to optimize the next waypoint considering not only the past waypoints already visited but also the future remaining trajectory of the UAV. Therefore, based on the analogy of noncausal filters in signal processing, the new proposed method is named as “noncausal trajectory optimization”. In the previous literature, because only the past trajectory is taken into account, the UAVs are mostly suggested to make circular or spiral motions around the target. However, this study suggests that the UAV must create a linear trajectory as much as possible when total travel length is shorter than initial distance to the target. For a single UAV, this study proves that the ideal direction of this linear path is only the function of the ratio of the total length of trajectory over the initial distance to the target. To prove this, the summation operators in Fisher information matrices (FIM) are converted to line integrals and then D-optimality criterion is applied. Next, noncausal trajectory optimization is introduced which is dedicated to obtain a higher final localization capability by sacrificing immediate or early advantages. Then, noncausal joint trajectory optimization for multiple UAVs is illustrated. Finally, trajectory optimization for moving target as well as three dimensional trajectory optimization are discussed. Monte Carlo simulations showed that the proposed optimization method obtains a higher final accuracy level compared to the ordinary trajectory optimization.

Trajectory optimization and resource allocation for UAV-assisted relaying communications

In this paper, we study the unmanned aerial vehicles (UAV) assisted relaying communication system, where a UAV acts the mobile relay and provides the information transfer from the source to the destination. The simultaneous wireless information and power transfer techniques are considered at the UAV relays, where the UAV harvests energy from the source node, and exclusively uses the harvested energy for the data relaying. To maximize the system throughput, we jointly consider the UAV trajectory optimization and resource allocation problem, where the UAV trajectory is optimized the UAV positions to harvest the benefits of the line-of-sight links, and resource allocation, including power allocation and the subcarrier allocation, is used to achieve optimal network performance with the power constraints. An alternating maximization algorithm is proposed to solve the optimization problem, in which the UAV trajectory optimization and resource allocation are solved iteratively to maximize the total throughput. Compared with other benchmarks, the proposed algorithm can achieve higher throughput by the benefits from UAV trajectory optimization and resource allocation.

Join trajectory optimization and communication design for UAV-enabled OFDM networks

Due to the advantages of high mobility, flexible maneuverability and fast deployment, Unmanned Aerial Vehicle (UAV), which can be usually served as aerial communication platform, not only supports information transmission in the Internet of things (IoT), but also provides reliable power supplement for low-power wireless devices. Considering an UAV-enabled Orthogonal Frequency Division Multiplexing (OFDM) network where subcarriers are divided into two groups for information transmission and energy harvesting, respectively, a joint UAV trajectory optimization and communication design scheme is proposed based on Simultaneous Wireless Information and Power Transfer (SWIPT) technology. Under the given average harvested energy for users, the objective of the scheme is to maximize the average achievable rate for all users by jointly optimizing UAV trajectory, user scheduling, subcarrier and power allocation. To solve the formulated optimization problem, it is transformed into two subproblems to optimize resource allocation and UAV trajectory, respectively. The former is a mixed integer non-convex optimization problem. To solve it, a three-variable alternative iteration algorithm is proposed to obtain the optimal user scheduling, subcarrier and power allocation. Since the latter is non-convex, it can be transformed into convex optimization problem by relaxing objective function and constraint to obtain the optimal trajectory. Simulation results demonstrate that the proposed algorithm has good convergence and the proposed UAV-enabled OFDM network has better performance.

UAV-Enabled Data Collection: Multiple Access, Trajectory Optimization, and Energy Trade-Off

In this paper, we consider a ground terminal (GT) to an unmanned aerial vehicle (UAV) wireless communication system where data from GTs are collected by an unmanned aerial vehicle. We propose to use the ground terminal-UAV (G-U) region for the energy consumption model. In particular, to fulfill the data collection task with a minimum energy both of the GTs and UAV, an algorithm that combines optimal trajectory design and resource allocation scheme is proposed which is supposed to solve the optimization problem approximately. We initialize the UAV’s trajectory firstly. Then, the optimal UAV trajectory and GT’s resource allocation are obtained by using the successive convex optimization and Lagrange duality. Moreover, we come up with an efficient algorithm aimed to find an approximate solution by jointly optimizing trajectory and resource allocation. Numerical results show that the proposed solution is efficient. Compared with the benchmark scheme which did not adopt optimizing trajectory, the solution we propose engenders significant performance in energy efficiency.

Iterative Trajectory Optimization for Physical-Layer Secure Buffer-Aided UAV Mobile Relaying.

With the fast development of commercial unmanned aerial vehicle (UAV) technology, there are increasing research interests on UAV communications. In this work, the mobility and deployment flexibility of UAVs are exploited to form a buffer-aided relaying system assisting terrestrial communication that is blocked. Optimal UAV trajectory design of the UAV-enabled mobile relaying system with a randomly located eavesdropper is investigated from the physical-layer security perspective to improve the overall secrecy rate. Based on the mobility of the UAV relay, a wireless channel model that changes with the trajectory and is exploited for improved secrecy is established. The secrecy rate is maximized by optimizing the discretized trajectory anchor points based on the information causality and UAV mobility constraints. However, the problem is non-convex and therefore difficult to solve. To make the problem tractable, we alternatively optimize the increments of the trajectory anchor points iteratively in a two-dimensional space and decompose the problem into progressive convex approximate problems through the iterative procedure. Convergence of the proposed iterative trajectory optimization technique is proved analytically by the squeeze principle. Simulation results show that finding the optimal trajectory by iteratively updating the displacements is effective and fast converging. It is also shown by the simulation results that the distribution of the eavesdropper location influences the security performance of the system. Specifically, an eavesdropper further away from the destination is beneficial to the system’s overall secrecy rate. Furthermore, it is observed that eavesdropper being further away from the destination also results in shorter trajectories, which implies it being energy-efficient as well.

Optimal 1D Trajectory Design for UAV-Enabled Multiuser Wireless Power Transfer

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) network, where a UAV flies at a constant altitude in the sky to provide wireless energy supply for a set of ground nodes with a linear topology. Our objective is to maximize the minimum received energy among all ground nodes by optimizing the UAV's one-dimensional (1D) trajectory, subject to the maximum UAV flying speed constraint. Different from previous works that only provided heuristic and locally optimal solutions, this paper is the first work to present the globally optimal 1D UAV trajectory solution to the considered min-energy maximization problem. Towards this end, we first show that for any given speed-constrained UAV trajectory, we can always construct a maximum-speed trajectory and a speed-free trajectory, such that their combination can achieve the same received energy at all these ground nodes. Next, we transform the original UAV-speed-constrained trajectory optimization problem into an equivalent UAV-speed-free problem, which is then optimally solved via the Lagrange dual method. The obtained optimal 1D UAV trajectory solution follows the so-called successive hover-and-fly (SHF) structure, i.e., the UAV successively hovers at a finite number of hovering points each for an optimized hovering duration, and flies among these hovering points at the maximum speed. Numerical results show that our proposed optimal solution significantly outperforms the benchmark schemes in prior works under different scenarios.

UAV trajectory optimization for Minimum Time Search with communication constraints and collision avoidance

Minimum Time Search (MTS) algorithms help in search missions proposing search trajectories that minimize the target detection time considering the available information about the search scenario. This work proposes a MTS planner based on ant colony optimization that includes communication and collision avoidance constraints. This ensures that the Unmanned Aerial Vehicles (UAVs) are able to complete the optimized search trajectories without risk of collision or loss of communication with the ground control station. This approach is a great advantage nowadays, where UAVs flight regulation is quite strict, often requiring to monitor the state of the UAVs during the whole mission, impeding UAV deployments without continuous communication to the ground control station. The proposed algorithm is tested with several search scenarios and compared against two state of the art techniques based on Cross Entropy Optimization and Genetic Algorithms, which have been adapted to make them consider collision and communication constraints as well.

Energy-Effective Data Gathering for UAV-Aided Wireless Sensor Networks.

Unmanned aerial vehicles (UAVs) are capable of serving as a data collector for wireless sensor networks (WSNs). In this paper, we investigate an energy-effective data gathering approach in UAV-aided WSNs, where each sensor node (SN) dynamically chooses the transmission modes, i.e., (1) waiting, (2) conventional sink node transmission, (3) uploading to UAV, to transmit sensory data within a given time. By jointly considering the SN’s transmission policy and UAV trajectory optimization, we aim to minimize the transmission energy consumption of the SNs and ensure all sensory data completed collected within the given time. We take a two-step iterative approach and decouple the SN’s transmission design and UAV trajectory optimization process. First, we design the optimal SNs transmission mode policy with preplanned UAV trajectory. A dynamic programming (DP) algorithm is proposed to obtain the optimal transmission policy. Then, with the fixed transmission policy, we optimize the UAV’s trajectory from the preplanned trace with recursive random search (RRS) algorithm. Numerical results show that the proposed scheme achieves significant energy savings gain over the benchmark schemes.

Trajectory Planning of the Unmanned Aerial Vehicles with Adaptive Convex Optimization Method

Abstract In this paper, a constrained unmanned aerial vehicles trajectory optimization problem is formulated. We present an iterative convex programming algorithm to solve the optimal control problem, which consists of both nonlinear dynamics and path constraints. A hp-adaptive pseudospectral method is introduced to transform the original problem into nonlinear programming. Furthermore, in order to improve the computational ability, nonlinear dynamics and path constraints are convexified by convexification techniques. The trajectory planning problem is formulated as a convex programming problem. We evaluate the proposed strategy by some numerical results. The results indicate that the algorithm can get better performance compared with other typical optimal control solvers.

Fast Trajectory Optimization using Sequential Convex Programming with No-Fly Zone Constraints

Abstract This paper proposes a new trajectory optimization method for Unmanned Aerial Vehicle (UAV). The proposed L1-Penalized Sequential Convex Programming (LPSCP) method improves the initial infeasibility of the standard sequential convex programming. LPSCP method converges within 0.2 seconds which is more than one-tenth of the pseudospectral (PS) method. Therefore, LPSCP has the potential to enable UAV’s real-time autonomous air mission if implemented on-board. The UAV trajectory optimization problem is defined at the beginning of the paper and a convexification process is performed when there are several no-fly zones along the trajectory. Then the LPSCP method iteratively solves locally approximated subproblems in conic forms. Simulation results illustrate the proposed method satisfies the required constraints and has a computation time advantage over the conventional PS method.

Deployment and Trajectory Optimization of UAVs: A Quantization Theory Approach

Optimal deployment and movement of multiple unmanned aerial vehicles (UAVs) are studied. The considered scenario consists of several ground terminals (GTs) communicating with the UAVs using variable transmission power and fixed data rate. First, the static case of a fixed geographical GT density is analyzed. Using a high-resolution quantization theory, the corresponding best achievable performance (measured in terms of the average GT transmission power) is determined in the asymptotic regime of a large number of UAVs. Next, the dynamic case where the GT density is allowed to vary periodically through time is considered. For 1-D networks, an accurate formula for the total UAV movement that guarantees the best time-averaged performance is determined. In general, the tradeoff between the total UAV movement and the achievable performance is obtained through a Lagrangian approach. A corresponding trajectory optimization algorithm is introduced and shown to guarantee a convergent Lagrangian. Numerical simulations confirm the analytical findings. Extensions to different system models and performance measures are also discussed.

Trajectory Design for Distributed Estimation in UAV-Enabled Wireless Sensor Network

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless sensor network, where a UAV is dispatched to collect the sensed data from distributed sensor nodes (SNs) for estimating an unknown parameter. It is revealed that in order to minimize the mean square error for the estimation, the UAV should collect the data from as many SNs as possible, based on which an optimization problem is formulated to design the UAV's trajectory subject to its practical mobility constraints. Although the problem is nonconvex and NP-hard, we show that the optimal UAV trajectory consists of connected line segments only. With this simplification, an efficient suboptimal solution is proposed by leveraging the classic traveling salesman problem and applying convex optimization techniques. Simulation results show that the proposed trajectory design achieves a significant performance gain in terms of the number of SNs whose data are successfully collected, as compared with other benchmark schemes.

UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells

In future mobile networks, it is difficult for static base stations (BSs) to support the rapidly increasing data services, especially for cell-edge users. Unmanned aerial vehicle (UAV) is a promising method that can assist BSs to offload the data traffic, due to its high mobility and flexibility. In this correspondence, we focus on the UAV trajectory at the edges of three adjacent cells to offload traffic for BSs. In the proposed scheme, the sum rate of UAV-served edge users is maximized subject to the rate requirements for all the users, by optimizing the UAV trajectory in each flying cycle. The optimization is a mixed-integer nonconvex problem, which is difficult to solve. Thus, it is transformed into two convex problems, and an iterative algorithm is proposed to solve it by optimizing the UAV trajectory and edge user scheduling alternately. Simulation results are presented to show the effectiveness of the proposed scheme.

Outage probability minimization for low-altitude UAV-enabled full-duplex mobile relaying systems

In this paper, a novel unmanned aerial vehicle (UAV)-enabled full duplex decode-and-forward (DF) technique is used in mobile relaying system. Compared with conventional static relaying, mobile relaying provides more degree of freedom for experiencing better channel conditions and further improving the system reliable performance. The source and relay transmit power as well as relay trajectory are jointly optimized for sum outage probability minimization in mobile relaying system. Due to the non-convex nature of the original problem, block coordinate decent optimization techniques are employed to decompose it into two sub-problems, which leads to an efficient iterative algorithm. Specifically, for the pre-determined relay trajectory, the optimal transmit power of source and relay are obtained by solving Karush-Kuhn-Tucker (KKT) conditions. For the given source/relay power allocation, the optimal UAV trajectory is obtained by solving dual problem. Based on the two steps, an iterative algorithm is proposed to jointly optimize source/relay power allocation and UAV trajectory alternately. Numerical results show the performance gain of our proposed scheme.

Trajectory Optimization and Power Allocation for Multi-Hop UAV Relaying Communications

Unmanned aerial vehicles (UAVs) have found important applications in wireless communications due to their ability for on-demand and swift deployment, high mobility, and high probability of line-of-sight communication link with the ground. For UAV-enabled wireless communications, one of the main use cases is UAV relaying, where UAVs are deployed as flying relays in the sky to provide wireless connections between distant users that do not have reliable direct connectivity. This paper considers a general multiple UAV relaying system, where the information from a source node to a destination node is forwarded through multi-hop UAV relays. We aim to maximize the end-to-end throughput via joint UAV trajectory and transmit power optimization, subject to UAV mobility and collision avoidance constraints, the information-causality constraints, and the average and peak transmit power constraints of the source and UAV relays. The formulated problem is non-convex and challenging to solve, hence we propose an efficient iterative algorithm to obtain a suboptimal solution based on the alternating maximization and successive convex optimization techniques. Numerical results validate the effectiveness of the proposed algorithm as compared to three benchmark schemes with only transmit power allocation or trajectory optimization.

Flight path optimization for an electronic intelligence unmanned aerial vehicle

The bearings-only tracking problem with angle-of-arrival (AoA) information received from unmanned aerial vehicle (UAV) is considered. The Kalman filter and joint integrated probabilistic data association filters (JIPDAF) are utilized for single or multiple targets tracking, respectively. An optimal UAV trajectory is constructed for both single and multiple target tracking scenarios. The special case of stationary radar stations localization is numerically simulated.

UAV Optimal Obstacle Avoidance while Respecting Target Arrival Specifications

AbstractEffective target tracking and obstacle avoidance strategies are essential to the success of unmanned aerial vehicle (UAV) missions. This paper describes a decentralized model-based predictive control to calculate the optimal UAV trajectory that will lead each UAV to the interception of a target at a desired time and with desired speed, heading and flight path angle, while avoiding dynamic ellipsoidal obstacles detected en route. Obstacle avoidance is based on the minimization of the UAV collision probability with all known obstacles on a future horizon, while ensuring that the collision probability with any given obstacle at each prediction step does not surpass a preset threshold. Cooperation between UAVs is possible by exchanging information about the obstacles they detect. To respect the arrival specifications, a virtual target is created and tracked. The virtual target moves at the desired speed and along a straight line that is correctly oriented to intercept the real target position at the desired arrival time. Arrival specifications are respected if tracking of the virtual target by the UAV is successful. Simulations are presented to demonstrate the effectiveness of the proposed approach. They also illustrate that cooperation can improve the UAV performance.