Efficient Trajectory Planning for UAVs Using Hierarchical Optimization

Shao, Shikai; He, Chenglong; Zhao, Yujia; Wu, Xiaojing

doi:10.1109/access.2021.3073420

Cited by 18 publications

(15 citation statements)

References 34 publications

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“…The dynamic model for the flock of UAVs was investigated to obtain the efficient trajectory planning via Improved Particle Swarm Optimization (IPSO) and Gauss Pseudo-spectral Method (GPM) 17 . Also, a hierarchical exploitation of swarm UAVs was studied for a reliable communication with ground base stations (GBSs) and a new protocol was introduced via choosing a swarm head among the UAVs to overcome the interference of occupied GBSs 18 .…”

Section: Related Workmentioning

confidence: 99%

Placement and Power Assignment for Hierarchical UAV Networks under Hovering Fluctuations in mmWave Communications

Abdollahi

Azarhava

Niya

2023

Preprint

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In this paper, we investigate the successful transmission probability of an aerial cellular network in which an unmanned aerial vehicle (UAV) as a Macrocell Base Station (UAV-BS) serves other UAVs as aerial users. The beamforming capable antennas are mounted on the UAVs, to increase the throughput of the network. The random effects of inner forces such as controlling errors or outer forces like the air conditions result in the random fluctuations. We assume Rician fading distribution over the links between the UAVs, then, we calculate the distribution of the channels under hovering fluctuations. Also, we derive the closed form expressions for successful transmission probability. Defining an optimization problem on the average successful transmission probability of the network, we obtain the best placement of UAV-BS along with the resource allocation. The problem turns out to be a non-convex problem and time consuming via numerical exhaustive search methods. Instead, we solve the optimization problem for its lower bound. Maximization problem for the achieved lower bound is equivalent to maximize the main problem. Then, we use some approximations to convert it to a low complex problem to find the solution. We use the entity of the low complex problem to obtain the allocated power for each UAV and in the following, the problem becomes convex which is solved by KKT conditions to obtain the location of UAV-BS. The theoretical results show that optimizing the lower bound probability achieves the suboptimal solution for power assignment and placement problem, which is verified by simulation results.

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Section: Related Workmentioning

confidence: 99%

Placement and Power Assignment for Hierarchical UAV Networks under Hovering Fluctuations in mmWave Communications

Abdollahi

Azarhava

Niya

2023

Preprint

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“…Inequality (33) shows that the distance between p static zmp and p dynamic zmp during turning motion is proportionally bounded by ( ψ2 + |u ψ |). Then, it can be inferred that there exists a positive value such that p dynamic zmp −p static zmp 2 ≤ σ for an arbitrarily small σ > 0 during turning motion as long as ( ψ2 + |u ψ |) ≤ .…”

Section: Conflictsmentioning

confidence: 99%

“…In recent years, hierarchical planning has been used with some success in autonomous driving and for UAVs, as demonstrated in refs. [30,31,32,33]. However, the aforementioned hierarchical planning examples only apply to the specific applications they were designed for and cannot accommodate the dynamic stability constraint.…”

Section: Introductionmentioning

confidence: 99%

Stability-constrained mobile manipulation planning on rough terrain

Song¹,

Sharf²

2022

Robotica

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This paper presents a hierarchical framework that allows online point-to-point dynamic-stability-constrained optimal trajectory planning of a mobile manipulator robot working on rough terrain. First, the kinematics model of a mobile manipulator robot and the zero moment point stability measure are presented as theoretical background. Then, a sampling-based quasi-static planning algorithm modified for stability guarantee and traction optimization in continuous dynamic motion is presented along with a mathematical proof. The robot’s quasi-static path is then used as an initial guess to warm start a nonlinear optimal control solver which may otherwise have difficulties finding a solution to the stability-constrained formulation efficiently. The performance and computational efficiency of the framework are demonstrated through an application to a simulated timber harvesting mobile manipulator machine working on varying terrain. The results demonstrate feasibility of online trajectory planning on varying terrain while satisfying the dynamic stability constraint. Qualitative and quantitative comparisons with existing methods are also presented.

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“…The results guaranteed the convergence and provided major improvements in energy efficiency and secrecy rate. Shao et al [77] linked multi-segment strategy with improved particle swarm optimization-Gauss pseudo-spectral method (IPSO-GPM) for UAV swarms. The outcomes evaluated that the applied mechanisms increased obtained solution optimality, generated high-quality trajectories, and took minimum running time.…”

Section: Related Surveymentioning

confidence: 99%

Motion Planning of UAV Swarm: Recent Challenges and Approaches

Iqbal¹,

Ali²,

Khan³

et al. 2022

Aeronautics - New Advances

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The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work.

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Efficient Trajectory Planning for UAVs Using Hierarchical Optimization

Cited by 18 publications

References 34 publications

Placement and Power Assignment for Hierarchical UAV Networks under Hovering Fluctuations in mmWave Communications

Placement and Power Assignment for Hierarchical UAV Networks under Hovering Fluctuations in mmWave Communications

Stability-constrained mobile manipulation planning on rough terrain

Motion Planning of UAV Swarm: Recent Challenges and Approaches

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