Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

Internet of remote things (IoRT) networks are regarded as an effective approach for providing services to smart devices, which are often remote and dispersed over in a wide area. Due to the fact that the ground base station deployment is difficult and the power consumption of smart devices is limited in IoRT networks, the hierarchical Space-Air-Ground architecture is very essential for these scenarios. This paper aims to investigate energy efficient resource allocation problem in a two-hop uplink communication for Space-Air-Ground Internet of remote things (SAG-IoRT) networks assisted with unmanned aerial vehicle (UAV) relays. In particular, the optimization goal of this paper is to maximize the system energy efficiency by jointly optimizing sub-channel selection, uplink transmission power control and UAV relays deployment. The optimization problem is a mix-integer non-linear non-convex programming, which is hard to tackle. Therefore, an iterative algorithm that combines two sub-problems is proposed to solve it. First, given UAV relays deployment position, the optimal sub-channel selection and power control policy are obtained by the Lagrangian dual decomposition method. Next, based on the obtained sub-channel allocation and power control policy, UAV relays deployment is obtained by successive convex approximation (SCA). These two sub-problems are alternatively optimized to obtain the maximum system energy efficiency. Numerical results verify that the proposed algorithm has at least 21.9% gain in system energy efficiency compared to the other benchmark scheme.

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Energy Efficient Resource Allocation for UAV-Assisted Space-Air-Ground Internet of Remote Things Networks

Aiming at the problem of UAV swarms with distributed subsets performing cooperative reconnaissance-and-attack tasks on multi-targets in complex and uncertain combat scenarios, a distributed grouping cooperative dynamic task assignment method is proposed based on extended contract network protocol. The dynamic task assignment model for the UAV swarm with the topology of distributed subsets is established considering multiple constraints such as task cooperation, performing sequence, dynamic environment, communication topology, payload model, and UAV capability. According to the characteristics of multi-participants and multi-tasks in the process of UAV swarm executing tasks, the determination mechanism on cooperators and the selection mechanism of sequential tasks are proposed, and then the contract network protocol is extended. On the basis of the above, an event-triggered task assignment strategy for dynamic tasks is designed. The simulated results show that the proposed method can achieve the cooperative dynamic assignment of the UAV swarm to perform reconnaissance-and-attack tasks to multi-targets in complex and uncertain combat scenarios, improve the adaptiveness of the swarm under the sudden circumstance, and realize the optimization for task execution efficiency of the UAV swarm.

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Distributed Grouping Cooperative Dynamic Task Assignment Method of UAV Swarm

This article focuses on the problem of adaptive safe stabilization for a class of nonlinear control affine systems with parameter uncertainties. Two novel definitions of adaptive control Lyapunov function (ACLF) and adaptive control barrier function (ACBF) are first introduced, which use multiple identification models combined with a logic switching mechanism for parameter adaptation. The switching mechanism can improve the transient performance of nonlinear systems. Also, a multiunmanned aerial vehicle (multi-UAV) safe collaborative transportation framework based on ACLF and ACBF is provided in the presence of parametric uncertainties, in which a nominal controller is derived by using the proposed ACLF with a quadratic programming (QP) algorithm, and the nominal controller is further modified by the ACBF-QP algorithm for safety filter to achieve adaptive safe stabilization. Finally, simulation results for safe collaborative transportation of dual unmanned aerial vehicles in three-dimensional space are presented to demonstrate the effectiveness of the proposed design approach.

Multi-UAV Safe Collaborative Transportation Based on Adaptive Control Barrier Function

This study aims to propose a technology forecasting approach based on hierarchical S-curves. The proposed approach uses holistic forecasting by evaluating the S-curves of sub-technologies as well as the main technology under concern. A case study of unmanned aerial vehicle (UAV) technologies is conducted to demonstrate how the proposed approach works in practice. This is the first study that applies hierarchical S-curves to technology forecasting of unmanned aerial vehicle technologies in the literature. The future trend of the UAV technologies is analysed in detail through a hierarchical S-curve approach. Hierarchical S-curves are also utilised to investigate the sub-technologies of the UAV. In addition, the technology development life cycle of technology is assessed by using the three indexes namely, (1) the current technological maturity ratio (TMR), (2) estimating the number of potential patents that could be granted in the future (PPA), and (3) forecasting the expected remaining life (ERL). The results of this study indicate that the UAV technologies and their sub-technologies are at the growth stage in the technology life cycle, and most of the developments in UAV technology will have been completed by 2048. Hence, these technologies can be considered emerging technologies.

Technology Forecasting of Unmanned Aerial Vehicle Technologies through Hierarchical S Curves

A hierarchic optimization strategy based on path planning and trajectory planning process is presented in this paper to solve the trajectory planning problem of multiple unmanned aerial vehicles(multi-UAVs). Firstly, a model of path planning considering obstacle constrains is established, and a variable step size Sparse A * Algorithm (SAS) is proposed to overcome the disadvantages of the fixed step size. And the path planning process is simulated. Secondly, The trajectory planning strategy is established based on Decentralized Model Predictive Control(DMPC). Taking UAV flight kinematics and collisions avoidance constraints into account, the DMPC single-step nonlinear programming model is established and solved by optimization toolbox of MATLAB. Compared with the traditional trajectory optimization methods, the strategy proposed is better at real-time to search a better solution.

A Hierarchical Optimization Strategy of Trajectory Planning for Multi-UAVs

The dynamic coalition task allocation of heterogeneous multiple UAV agents is researched, which is divided into two parts. Firstly, the consensus based coalition algorithm(CBCA) is presented via consensus based bundle algorithm(CBBA), considering complex constraints of specific equipment requirements and coupling the relationships between the subtasks and the time windows. Secondly, three dynamic planning strategies are proposed in cope with appearance of new tasks during the allocation process. Finally, the feasibility and applicability of the present algorithm and dynamic planning strategies are validated in the scenario of a search and attack mission executed by multiple unmanned search aerial vehicles(USAVs) and unmanned combat aerial vehicles (UCAVs).

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Dynamic Coalition Task Allocation of Heterogeneous Multiple Agents

Efficient and flexible mission system structure plays a key role in the cooperative operation of Manned/unmanned Aerial Vehicle Formation(MAVF). The software architecture of mission system for MAVF is studied in this paper. Firstly, the command and control (C2) system architecture is proposed based on the Multi-Agent System(MAS) after the quantitative analysis evaluation and simulated with MATLAB. Secondly, the software architecture of mission system based on the Service-Oriented Architecture(SOA) model is constructed, and the seven-level software system based on service is designed to improve the flexibility and reusability of the system.

Software architecture of C2 mission system based on MAS and SOA for manned/unmanned aerial vehicle formation

To cope with the uncertainties of position and arrival time of UAVs during trajectory planning, a trajectory optimization method considering timing constraints to eliminate collisions in the multi-UAV cooperated ground attack mission. Firstly, characteristics of multi-UAV coordinated ground attack missions are analyzed, and a mission model of coordinated attacking is established. Secondly, a trajectory collision degree function is introduced. Collisions can be eliminated by changing control variables of flight time, trajectory and flight height. Simulation results show feasibility and applicability of the proposed method in solving the collision-free trajectory planning problem.

Collision-Free Trajectory Planning for Multi-UAV Coordinated Ground Attack Mission Under Uncertainties

As a challenging and highly complex problem, the trajectory planning for unmanned combat aerial vehicle (UCAV) focuses on optimising flight trajectory under such constraints as kinematics and complicated battlefield environment. An online case-based trajectory planning strategy is proposed in this study to achieve rapid control variables solution of UCAV flight trajectory for the of delivery airborne guided bombs. Firstly, with an analysis of the ballistic model of airborne guided bombs, the trajectory planning model of UCAVs is established with launch acceptable region (LAR) as a terminal constraint. Secondly, a case-based planning strategy is presented, which involves four cases depending on the situation of UCAVs at the current moment. Finally, the feasibility and efficiency of the proposed planning strategy is validated by numerical simulations, and the results show that the presented strategy is suitable for UCAV performing airborne guided delivery missions in dynamic environments.

https://publications.drdo.gov.in/ojs/index.php/dsj/article/download/15040/7320

A Case based Online Trajectory Planning Method of Autonomous Unmanned Combat Aerial Vehicles with Weapon Release Constraints

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