A vision-based landing system for small unmanned aerial vehicles using an airbag

Statistics show that the landing accounts for the largest portion of all mishaps of unmanned aerial vehicles (UAVs) due to many difficulties including limited situational awareness of the external pilot and the limited maneuverability during the low speed flight before touchdown. In this paper, a vision-based automatic landing system using a dome-shaped airbag is proposed for small UAVs. Its isotropic shape allows airplanes to approach in any direction to avoid crosswind unlike net-assisted landing. The dome’s distinctive color improves the detection owing to its strong visual cue. Color- and shape-based detection vision algorithms are applied for robust detection under varying lighting conditions. Due to the insufficient accuracy of navigation sensors, a direct visual servoing is used for terminal guidance. The proposed algorithm is validated in a series of flight tests.     

Enabling landings on irregular surfaces for unmanned aerial vehicles via a novel robotic landing gear

Intelligent Service Robotics - Unmanned aerial vehicles (UAVs) have been attracting much attention and changing our daily lives. Recent technological advances in the development of UAVs have...     

Vision-based navigation of unmanned aerial vehicles

This paper presents a vision-based navigation strategy for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using a single embedded camera observing natural landmarks. In the proposed approach, images of the environment are first sampled, stored and organized as a set of ordered key images (visual path) which provides a visual memory of the environment. The robot navigation task is then defined as a concatenation of visual path subsets (called visual route) linking the current observed image and a target image belonging to the visual memory. The UAV is controlled to reach each image of the visual route using a vision-based control law adapted to its dynamic model and without explicitly planning any trajectory. This framework is largely substantiated by experiments with an X4-flyer equipped with a fisheye camera.     

Nonlinear path-following method for fixed-wing unmanned aerial vehicles

A path-following method for fixed-wing unmanned aerial vehicles(UAVs) is presented in this paper.This method consists of an outer guidance loop and an inner control loop.The guidance law relies on the idea of tracking a virtual target.The motion of the virtual target is explicitly specified.The main advantage of this guidance law is that it considers the maneuvering ability of the aircraft.The aircraft can asymptotically approach the defined path with smooth movements.Meanwhile,the aircraft can anticipate the upcoming transition of the flight path.Moreover,the inner adaptive flight control loop based on attractive manifolds can follow the command generated by the outer guidance loop.This adaptive control law introduces a first-order filter to avoid solving the partial differential equation in the immersion and invariance adaptive control.The performance of the proposed path-following method is validated by the numerical simulation.     

Motion control problems for multimode unmanned aerial vehicles

This paper considers flight control specifics for modern unmanned aerial vehicles with paths described by a wide range of altitudes, including super-aerodynamic flights. It is noted that this type of aerial vehicles belongs to multimode dynamic objects with varying characteristics in the course of their operation. Adaptive control methods used in control systems for unmanned aerial vehicles are overviewed. Finally, adaptive control capabilities are studied for the vehicle’s angular position in gliding and cruising modes under combined jet reaction and aerodynamic control.     

Flocking control of a fleet of unmanned aerial vehicles

Current applications using single unmanned vehicle have been gradually extended to multiple ones due to their increased efficiency in mission accomplishment, expanded coverage areas and ranges, as well as enhanced system reliability. This paper presents a flocking control method with application to a fleet of unmanned quadrotor helicopters (UQHs). Three critical characteristics of formation keeping, collision avoidance, and velocity matching have been taken into account in the algorithm development to make it capable of accomplishing the desired objectives (like forest/pipeline surveillance) by safely and efficiently operating a group of UQHs. To achieve these, three layered system design philosophy is considered in this study. The first layer is the flocking controller which is designed based on the kinematics of UQH. The modified Cucker and Smale model is used for guaranteeing the convergence of UQHs to flocking, while a repelling force between each two UQHs is also added for ensuring a specified safety distance. The second layer is the motion controller which is devised based on the kinetics of UQH by employing the augmented state-feedback control approach to greatly minimize the steady-state error. The last layer is the UQH system along with its actuators. Two primary contributions have been made in this work: first, different from most of the existing works conducted on agents with double integrator dynamics, a new flocking control algorithm has been designed and implemented on a group of UQHs with nonlinear dynamics. Furthermore, the constraint of fixed neighbouring distance in formation has been relaxed expecting to significantly reduce the complexity caused by the increase of agents number and provide more flexibility to the formation control. Extensive numerical simulations on a group of UQH nonlinear models have been carried out to verify the effectiveness of the proposed method.     

低小慢无人机降落野外场景识别方法

针对低小慢无人机野外飞行场景复杂自主降落场景识别问题,提出了一种融合局部金字塔特征和卷积神经网络学习特征的野外场景识别算法。首先,将场景分为4×4和8×8块的小场景,使用方向梯度直方图（HOG）算法提取所有块的场景特征,所有特征首尾连接得到具有空间金字塔特性的特征向量。其次,设计一个针对场景分类的深度卷积神经网络,采用调优训练方法得到卷积神经网络模型,并提取深度网络学习特征。最后,连接两个特征得到最终场景特征,并使用支持向量机（SVM）分类器进行分类。所提算法在Sports-8、Scene-15、Indoor-67以及自建数据集上较传统手工特征方法的识别准确率提高了4个百分点以上。实验结果表明,所提算法能有效提升降落场景识别准确率。     

基于行为协同和虚拟目标相结合的无人机实时航路规划

针对实时航路规划问题,综合考虑航路最优、平滑性、全局收敛性以及从威胁域的逃逸能力等限制时,还没有有效的规划算法.为此提出了一种基于行为协同和虚拟目标相结合的无人机实时航路规划方法.该方法将无人机的航路规划行为分为局部和全局行为:局部行为采用基于模糊控制的方法,用来实现威胁体规避;全局行为使用全局算法,通过全局目标和虚拟目标的切换实现了全局目标收敛和威胁域边界跟踪,然后通过模糊控制器对两种行为进行协同.最后通过分析、证明以及几种不同情形下的仿真表明该方法具有航路短、平滑和全局收敛的特点.     

Distributed output-feedback formation tracking control for unmanned aerial vehicles

This paper considers the output-feedback formation problem of tracking a desired trajectory for a group of networked unmanned aerial vehicles (UAVs). By introducing a state observer, the controller for the non-holonomic UAV model can be designed without linear and angular velocities measurements. The formation robustness can be improved by applying the virtual structure and synchronising the path parameters. It is proved that, with the proposed control strategy, all the closed-loop signals are bounded and the formation tracking errors asymptotically converge to zero. Simulation results are given to illustrate the effectiveness of the proposed control strategy.     

Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles

In this paper, an efficient strategy is proposed to design the altitude hold mode autopilot for a UAV which is non-minimum phase, and its model includes both parametric uncertainties and unmodeled nonlinear dynamics. This work has been motivated by the challenge of developing and implementing an autopilot that is robust with respect to these uncertainties. By combination of classic controller as the principal section of the autopilot and the fuzzy logic controller to increase the robustness in a single loop scheme, it is tried to exploit both methods advantages. The multi-objective genetic algorithm is used to mechanize the optimal determination of fuzzy logic controller parameters based on an efficient cost function that comprises undershoot, overshoot, rise time, settling time, steady state error and stability. Simulation results show that the proposed strategy performances are desirable in terms of the time response characteristics for both phugoid mode and short period mode, the robustness, and the adaptation of itself with respect to the large commands.     

多无人机目标追踪自适应控制

针对多无人机自动驾驶仪速度与航向角通道系数未知的目标轨迹追踪问题,提出一种自适应追踪控制方法.通过对自动驾驶仪通道系数进行在线估计,解决了系数未知所造成的设计困难.为克服外界干扰及系统内部误差因素对无人机运动控制系统的影响,设计了补偿项来消除干扰项的影响.采用Lyapunov定理证明了轨迹追踪误差最终可收敛于任意小的区域内.仿真结果验证了所设计方法的有效性.     

无人机非确定性等价自适应容错飞行控制

针对存在执行器复合故障的固定翼无人机跟踪控制问题,本文提出一种基于非确定性等价原理的自适应容错飞行控制策略.该策略能够有效地估计无人机纵向动态中执行器的失效及漂移故障,保证故障发生后闭环系统的最优性能指标.在自适应容错飞行控制设计中,通过引入辅助系统并动态调节因子,构造非确定性等价原理中偏微分方程的近似解,以简化自适应律设计复杂度.此外,借助Lyapunov稳定性分析方法,证明了在所设计的自适应容错控制器作用下闭环系统的稳定性.最后,仿真验证表明所设计的控制方法能够保证故障无人机的闭环系统性能.     

Automatic terminal guidance for small fixed-wing unmanned aerial vehicles

The research presented in this article focuses on expanding and deepening the prior research of a low-cost terminal guidance system in a previous paper entitled “Design, implementation and verification of a low-cost terminal guidance system for small fixed-wing UAVs.” An automatic terminal guidance workflow is specially designed for an individual in a small fixed-wing unmanned aerial vehicle (SUAV) swarm. The extended work around the proposed workflow primarily involves upgrading onboard hardware modules to improve sensor accuracy and environmental adaptability, the imaging performance of the seeker, as well as the computational capability of the image processor, applying object detection to replace the human-in-the-loop function and adopting the integral proportional guidance law in the vertical direction to reduce the required overload and obtain a larger impact angle. Furthermore, we conducted several field tests on two types of SUAV against a stationary target on the ground in a field scenario. The experiments have generated valuable onboard image data and SUAV status information, all of which are aligned in the time domain. The only remaining data sets that support the findings of this study are available from the corresponding author. Our study into automatic terminal guidance has yielded a solution of the automatic strap-down monocular terminal guidance problem of individual SUAVs. The field trials of a single SUAV demonstrate the robustness and efficiency of the proposed automatic terminal guidance methodology and lays a foundation for the future SUAVs' cooperative attack test.     

Autopilots for small unmanned aerial vehicles: A survey

This paper presents a survey of the autopilot systems for small or micro unmanned aerial vehicles (UAVs). The objective is to provide a summary of the current commercial, open source and research autopilot systems for convenience of potential small UAV users. The UAV flight control basics are introduced first. The radio control system and autopilot control system are then explained from both the hardware and software viewpoints. Several typical off-the-shelf autopilot packages are compared in terms of sensor packages, observation approaches and controller strengths. Afterwards some open source autopilot systems are introduced. Conclusion is made with a summary of the current autopilot market and a remark on the future development.     

Extreme learning machine terrain-based navigation for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) rely on global positioning system (GPS) information to ascertain its position for navigation during mission execution. In the absence of GPS information, the capability of a UAV to carry out its intended mission is hindered. In this paper, we learn alternative means for UAVs to derive real-time positional reference information so as to ensure the continuity of the mission. We present extreme learning machine as a mechanism for learning the stored digital elevation information so as to aid UAVs to navigate through terrain without the need for GPS. The proposed algorithm accommodates the need of the on-line implementation by supporting multi-resolution terrain access, thus capable of generating an immediate path with high accuracy within the allowable time scale. Numerical tests have demonstrated the potential benefits of the approach.     

多无人机分布式协同异构任务分配

研究异构无人机对不同类型目标执行侦察、打击和评估任务的协同任务分配问题.采用信息论中熵的变化量对侦察与评估任务中所获取的信息量进行度量,将无人机对不同类型目标的打击能力抽象为对目标的毁伤概率,并考虑各个任务之间的相互关联,建立异构多无人机协同任务分配模型.设计了基于相邻局部通信的分布式拍卖算法,实现了多无人机协同任务分配问题的优化求解.仿真结果表明了所建模型的合理性和求解方法的有效性.     

基于多智能体的太阳能无人机能源控制

为满足太阳能无人机对长航时飞行和高载重能力的需求,研究能源系统的储能均衡控制问题.通过将太阳能无人机中每个由光伏-储能-输出单元组成的发电节点作为一个智能体,设计基于多智能体的分布式控制器并给出满足系统约束的控制算法,实现储能单元荷电状态的一致性.分别针对连续系统模型和离散系统模型给出分布式控制协议,并通过理论分析说明连续和离散的控制协议均可实现控制目标.通过搭建半实物平台进行实测验证,采用光伏模拟器和电子负载模拟能源系统运行的外部环境,以18650锂离子电池作为储能单元,实验结果表明,分布式协同控制协议能够有效地解决光伏功率不均及电池参数差异导致的不均衡问题,使系统的充放电深度得以有效提升.     

Decentralized overlapping control of a formation of unmanned aerial vehicles

Decentralized overlapping feedback laws are designed for a formation of unmanned aerial vehicles. The dynamic model of the formation with an information structure constraint in which each vehicle, except the leader, only detects the vehicle directly in front of it, is treated as an interconnected system with overlapping subsystems. Using the mathematical framework of the inclusion principle, the interconnected system is expanded into a higher dimensional space in which the subsystems appear to be disjoint. Then, at each subsystem, a static state feedback controller is designed to robustly stabilize the perturbed nominal dynamics of the subsystem. The design procedure is based on the application of convex optimization tools involving linear matrix inequalities. As a final step, the decentralized controllers are contracted back to the original interconnected system for implementation.     

Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

This paper proposes a behaviour recognition methodology for ground vehicles moving within road traffic using unmanned aerial vehicles in order to identify suspicious or abnormal behaviour. With the target information acquired by unmanned aerial vehicles and estimated by filtering techniques, ground vehicle behaviour is first classified into representative driving modes, and then a string pattern matching theory is applied to detect suspicious behaviours in the driving mode history. Furthermore, a fuzzy decision-making process is developed to systematically exploit all available information obtained from a complex environment and confirm the characteristic of behaviour, while considering spatiotemporal environment factors as well as several aspects of behaviours. To verify the feasibility and benefits of the proposed approach, numerical simulations on moving ground vehicles are performed using realistic car trajectory data from an off-the-shelf traffic simulation software.     

Autonomous landing on a moving vehicle with an unmanned aerial vehicle

This paper addresses the perception, control, and trajectory planning for an aerial platform to identify and land on a moving car at 15 km/hr. The hexacopter unmanned aerial vehicle (UAV), equipped with onboard sensors and a computer, detects the car using a monocular camera and predicts the car future movement using a nonlinear motion model. While following the car, the UAV lands on its roof, and it attaches itself using magnetic legs. The proposed system is fully autonomous from takeoff to landing. Numerous field tests were conducted throughout the year‐long development and preparations for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 competition, for which the system was designed. We propose a novel control system in which a model predictive controller is used in real time to generate a reference trajectory for the UAV, which are then tracked by the nonlinear feedback controller. This combination allows to track predictions of the car motion with minimal position error. The evaluation presents three successful autonomous landings during the MBZIRC 2017, where our system achieved the fastest landing among all competing teams.