Autonomous landing at unprepared sites by a full-scale helicopter

Helicopters are valuable since they can land at unprepared sites; however, current unmanned helicopters are unable to select or validate landing zones (LZs) and approach paths. For operation in unknown terrain it is necessary to assess the safety of a LZ. In this paper, we describe a lidar-based perception system that enables a full-scale autonomous helicopter to identify and land in previously unmapped terrain with no human input.We describe the problem, real-time algorithms, perception hardware, and results. Our approach has extended the state of the art in terrain assessment by incorporating not only plane fitting, but by also considering factors such as terrain/skid interaction, rotor and tail clearance, wind direction, clear approach/abort paths, and ground paths.In results from urban and natural environments we were able to successfully classify LZs from point cloud maps. We also present results from 8 successful landing experiments with varying ground clutter and approach directions. The helicopter selected its own landing site, approaches, and then proceeds to land. To our knowledge, these experiments were the first demonstration of a full-scale autonomous helicopter that selected its own landing zones and landed.     

Analysis of two-tier public service systems under a government subsidy policy

Some public service systems such as healthcare systems consist of both free public service provider with a long wait time and paid private service provider with a short wait time. Such service systems are often called a two-tier service system. In general, more customers will choose the free service provider (SP). To reduce the congestion in the free system, the government may encourage customers to use the pay system by offering them a subsidy. This paper studies whether such a subsidy can reduce the free system’s waiting time and improves the social welfare. While the objective of the free system is to maximize its own total customers’ utility, the objective of the pay system is to maximize its profit. We develop a mixed duopoly game to analyze the Nash equilibrium for the competition between the free and toll systems. Two scenarios with unregulated and regulated prices are considered. When the pay system price is unregulated (the private SP can set prices freely), we find that if customers are less sensitive to the service delay, the subsidy policy can effectively reduce the expected waiting time for the free system and increase the customer utility surplus of the entire two-tier system. However, if customers are more sensitive to the service delay, the subsidy policy may have the opposite effect. When the pay system price is regulated (the price determined by government), the subsidy policy can effectively reduce the expected waiting time for the free system and improve the social welfare of the two-tier system. And there exists an optimal regulated price to maximize the social welfare of the entire public service system.     

Application of generalised neural network for aircraft landing control system

 It is observed that landing performance is the most typical phase of an aircraft performance. During landing operation the stability and controllability are the major considerations. To achieve a safe landing, an aircraft has to be controlled in such a way that its wheels touch the ground comfortably and gently within the paved surface of the runway. The conventional control theory found very successful in solving well defined problems, which are described precisely with definite and clearly mentioned boundaries. In real life systems the boundaries can't be defined clearly and conventional controller does not give satisfactory results. Whenever, an aircraft deviates from its glide path (gliding angle) during landing operation, it will affect the landing field, landing area as well as touch down point on the runway. To control correct gliding angle (glide path) of an aircraft while landing, various traditional controllers like PID controller or state space controller as well as maneuvering of pilots are used, but due to the presence of non-linearities of actuators and pilots these controllers do not give satisfactory results. Since artificial neural network can be used as an intelligent control technique and are able to control the correct gliding angle i.e. correct gliding path of an aircraft while landing through learning which can easily accommodate the aforesaid non-linearities. The existing neural network has various drawbacks such as large training time, large number of neurons and hidden layers required to deal with complex problems. To overcome these drawbacks and develop a non-linear controller for aircraft landing system a generalized neural network has been developed.     

An experimental study of pilots' control characteristics for flight of an STOL aircraft in backside of drag curve at approach and landing.

In general, most vehicles can be modelled by a multi-variable system which has interactive variables. It can be clearly shown that there is an interactive response in an aircraft's velocity and altitude obtained by stick control and/or throttle control. In particular, if the flight conditions fall to backside of drag curve in the flight of an STOL aircraft at approach and landing then the ratio of drag variation to velocity change has a negative value (delta D/delta u less than 0) and the system of motion presents a non-minimum phase. Therefore, the interaction between velocity and altitude response becomes so complicated that it affects to pilot's control actions and it may be difficult to control the STOL aircraft at approach and landing. In this paper, experimental results of a pilot's ability to control the STOL aircraft are presented for a multi-variable manual control system using a fixed ground base simulator and the pilot's control ability is discussed for the flight of an STOL aircraft at backside of drag curve at approach and landing.     

基于深度学习的四旋翼无人机地面效应补偿降落控制设计

针对四旋翼无人机在降落控制过程中地面效应对控制性能有较大影响的问题,在地面效应复杂,难以建立机理模型的约束下,提出一种基于深度学习的新型非线性鲁棒控制策略.利用深度神经网络的学习能力,建立无人机降落过程中未知地面效应的补偿模型;结合super-twisting控制设计,实现对降落过程中未知地面效应的快速抑制和无人机降落的精确控制;通过Lyapunov分析法和谱归一化法,证明降落过程中闭环系统的稳定性和无人机位置误差的有限时间收敛特性.实时飞行实验结果表明,所提出的控制策略具有较好的控制效果.     

Ellipse proposal and convolutional neural network discriminant for autonomous landing marker detection

Autonomous landing in complex environments is a critical problem for unmanned aerial vehicle autonomous control, and efficiently detecting landing identification mark in real‐world scenario is still challenging. Due to the limited computational power of airborne computing equipment, current target detection algorithms cannot meet the demand efficiently. In this paper, we proposed a new landing marker detection algorithm for autonomous landing systems in a real environment. We used an ellipse detection algorithm to detect the ellipse landmark or other elliptical objects. Furthermore, convolution neural networks were utilized to obtain the correct landmarks, which is robust, fast and can achieve a speed of 25 fps with 720p resolution video on an Intel NUC onboard computer. Unlike the other methods, the accuracy and speed of our algorithm is verified in a real‐time application with more harsh conditions. During system testing, the flight system can detect the object above 20 m, track it, and automatically land on it with this vision algorithm. The algorithm has helped us achieve the first position at Mohamed Bin Zayed International Robotics Challenge in Abu Dhabi this year.     

舰载机前起落架突伸的动力学响应分析

为研究舰载机前轮拖拽弹射起飞技术,基于多体系统动力学理论,建立模拟舰载机前起落架突伸的4自由度多体动力学模型,推导出系统的动力学微分方程.利用该模型,用数值方法仿真舰载机前起落架的突伸运动,得出突伸过程中舰载机的重心位置、俯仰角和前起落架空气弹簧力等参数的动态响应特性.仿真结果可为舰载机及其起落架设计提供参考.     

飞机着陆全面控制的直接反馈线性化设计及仿真

应用直接反馈线性化的方法来设计非线性飞机模型自动着陆全面控制系统。首先以飞机原始非线性十二阶微分方程为对象,利用直接反馈线性化(DFL)理论对飞机原始非线性方程进行线性化,将原系统等效于一个六阶的线性系统。然后设计了自动着陆控制系统控制律,并进行了仿真,仿真结果满足飞机自动着陆各项指标要求,表明所设计的自动着陆控制系统具有较强的鲁棒性。     

A stochastic model on an additional warranty service contract

In general, a newly purchased item or system is warranted for a specific period. When the system fails during the warranty period, it is repaired free of charge. Even if the system is repairable, there exist some warranty services under which the manufacturer replaces the failed system during the warranty period. This study considers a case where a manufacturer offers an additional warranty service under which the failed system is replaced by a new one for its first failure, but minimal repairs are carried out to the system for its succeeding failures before the warranty expires. In this paper, we propose a mathematical model for setting a suitable charge of such an additional warranty service. Numerical examples assuming a personal computer are also presented.     

An experimental study of pilots' control characteristics for flight of an STOL aircraft in backside of drag curve at approach and landing

Abstract

In general, most vehicles can be modelled by a multi-variable system which has interactive variables. It can be clearly shown that there is an interactive response in an aircraft's velocity and altitude obtained by stick control and/or throttle control. In particular, if the flight conditions fall to backside of drag curve in the flight of an STOL aircraft at approach and landing then the ratio of drag variation to velocity change has a negative value (ΔD/Δu<0) and the system of motion presents a non-minimum phase. Therefore, the interaction between velocity and altitude response becomes so complicated that it affects to pilot's control actions and it may be difficult to control the STOL aircraft at approach and landing.

In this paper, experimental results of a pilot's ability to control the STOL aircraft are presented for a multi-variable manual control system using a fixed ground base simulator and the pilot's control ability is discussed for the flight of an STOL aircraft at backside of drag curve at approach and landing.     

Planning Stable and Efficient Paths for Reconfigurable Robots On Uneven Terrain

This paper proposes a novel path planning method for improving the feasibility of a forced landing. When an aircraft completely loses its thrust, the only measure it can take is to make a forced landing at an adjacent airport as soon as possible. In such a situation, the flight path to the landing point must be safe and viable. This paper details a method which enables safer and easier landing by transferring the benefits of excess altitude to the final approach length. Moreover, by planning the descent angle of final approach to be in the middle of a non-spoiler and a full-spoiler glide angle, this method enables a change in descent angle to correct any tracking errors, without using thrust. To verify the effectiveness of the proposed method, six degrees-of-freedom nonlinear simulations were performed and the results are compared with comparable methods. From the simulation results, it was confirmed that the proposed method could plan a safe path in a sufficiently short time and the aircraft could reach the landing point safely.     

Optimal subset-difference broadcast encryption with free riders

Broadcast encryption (BE) deals with secure transmission of a message to a group of receivers such that only an authorized subset of receivers can decrypt the message. The transmission cost of a BE system can be reduced considerably if a limited number of free riders can be tolerated in the system. In this paper, we study the problem of how to optimally place a given number of free riders in a subset-difference (SD)-based BE system, which is currently the most efficient BE scheme in use and has also been incorporated in standards, and we propose a polynomial-time optimal placement algorithm and three more efficient heuristics for this problem. Simulation experiments show that SD-based BE schemes can benefit significantly from the proposed algorithms.     

应用马尔科夫模型分析起落架的可靠性

为了提高飞机起落架控制系统的安全性能,提出了基于马尔科夫模型的起落架控制系统可靠性评估方法。首先分析系统每个节点的可靠性。运用马尔科夫过程对节点控制系统进行数学建模,根据故障空间状态图推导出系统的查普曼-柯尔莫戈洛方程,然后利用数学方法解出系统处于每一个状态的概率,得到节点控制系统的可靠度,进而求出整个起落架控制系统的可靠度。结果表明马尔科夫过程可以对起落架控制系统的可靠性进行准确的分析,结果精确,计算过程简单,对数学工具的要求低,适用于起落架这种复杂控制系统的可靠性研究。     

Autonomous real-time landing site selection for Venus and Titan using Evolutionary Fuzzy Cognitive Maps

Future science-driven landing missions, conceived to collect in situ data on regions of planetary bodies that have the highest potential to yield important scientific discoveries, will require a higher degree of autonomy. The latter includes the ability of the spacecraft to autonomously select the landing site using real-time data acquired during the descent phase. This paper presents the development of an Evolutionary Fuzzy Cognitive Map (E-FCM) model that implements an artificial intelligence system capable of autonomously selecting a landing site with the highest potential for scientific discoveries constrained by the requirement of soft landing in a region with safe terrains. The proposed E-FCM evolves its internal states and interconnections as a function of real-time data collected during the descent phase, therefore improving the decision process as more accurate information becomes available. The E-FCM is constructed using knowledge accumulated by planetary experts and it is tested on scenarios that simulate the decision process during the descent phase toward the Hyndla Regio on Venus. The E-FCM is shown to quickly reach conclusions that are consistent with what would be the choice of a planetary expert if the scientist were presented with the same information. The proposed methodology is fast and efficient and may be suitable for on-board spacecraft implementation and real-time decision making during the course of robotic exploration of the Solar System.     

Admission control for a responsive distributed middleware using decision trees to model run-time parameters

The software in modern systems has become too complex to make accurate predictions about their performance under different configurations. Real-time or even responsiveness requirements cannot be met because it is not possible to perform admission control for new or changing tasks if we cannot tell how their execution affects the other tasks already running. Previously, we proposed a resource-allocation middleware that manages the execution of tasks in a complex distributed system with real-time requirements. The middleware behavior can be modeled depending on the configuration of the tasks running, so that the performance of any given configuration can be calculated. This makes it possible to have admission control in such a system, but the model requires knowledge of run-time parameters. We propose the utilization of machine-learning algorithms to obtain the model parameters, and be able to predict the system performance under any configuration, so that we can provide a full admission control mechanism for complex software systems. In this paper, we present such an admission control mechanism, we measure its accuracy in estimating the parameters of the model, and we evaluate its performance to determine its suitability for a real-time or responsive system.     

飞机起落架收放系统性能仿真与故障分析

由于飞机前起落架与主起落架收放方向不同导致前起落架更容易受气动阻力影响而产生收放不到位等故障.对此利用AMEsim对某型飞机前起落架进行建模仿真,并通过建立起落架机械模型完成作动筒受力分析,将一些单一因素和混合因素分别注入到模型中对起落架收放性能进行仿真实验.实验结果表明,单一因素中油泵泄漏、作动筒限流阀阻塞、作动筒内...     

On centralized optimal control

It can be argued that the Holy Grail of control theory is the determination of the optimal feedback control law or simply the feedback control law. This is understandable given the huge success of the linear quadratic Gaussian (LQG) theory and applications for the past half-century. It is not an exaggeration to say that the entire aerospace industry, from the Apollo moon landing to the latest global positioning system (GPS), owe a debt to this control-theoretic development in the late 1950s and early 1960s. As a result, the curse of dimensionality notwithstanding, finding the optimal control law for more general dynamic systems remains an idealized goal for all problem solvers. We continue to hope that with each advance in computer hardware and mathematical theory, we will move one step closer to this ultimate goal. Efforts such as feedback linearization and multimode adaptive control can be viewed as such successful attempts. It is the thesis of this note to argue that this idealized goal of control theory is somewhat misplaced. We have been seduced by our early successes with the LQG theory and its extensions. The simple but often not emphasized fact is this: It is extremely difficult to specify and impossible to implement a general multivariable function even if the function is known.     

Aircraft landing problems with aircraft classes

This article focuses on the aircraft landing problem that is to assign landing times to aircraft approaching the airport under consideration. Each aircraft’s landing time must be in a time interval encompassing a target landing time. If the actual landing time deviates from the target landing time additional costs occur which depend on the amount of earliness and lateness, respectively. The objective is to minimize overall cost. We consider the set of aircraft being partitioned into aircraft classes such that two aircraft of the same class are equal with respect to wake turbulence. We develop algorithms to solve the corresponding problem. Analyzing the worst case run-time behavior, we show that our algorithms run in polynomial time for fairly general cases of the problem. Moreover, we present integer programming models. We show by means of a computational study how optimality properties can be used to increase efficiency of standard solvers.     

Cycle‐by‐Cycle Adaptive Force Compensation for the Soft‐Landing Control of an Electro‐Mechanical Engine Valve Actuator

Electro‐mechanical valve actuators (EMVA) are a solution for implementing variable valve actuation in internal combustion engines. Their use can increase engine power, reduce fuel consumption and pollutant emissions, while significantly improving engine efficiency. The control of this actuator is a complex task since non‐smooth nonlinearities, parameter variations and external forces strongly affect plant dynamics. In addition, the impact of the valve at its end‐strokes translates into mechanical wear and unacceptable noise, and in the worst case the electromagnet may also fail to catch the valve, causing system failure. The design of effective control strategies to ensure valve capture with low impact velocities is therefore essential for the correct functioning of such a mechatronic device. In this paper, the control problem of reducing the impact velocity at “landing” known in the literature as soft landing control, is tackled via novel cycle‐by‐cycle adaptive force compensation control algorithms. Two schemes are presented: a discrete adaptive proportional integral controller to regulate landing velocity to a preassigned set‐point, and a gradient descent method based controller to automatically achieve the minimum admissible impact velocity. The effectiveness of both methods in limiting landing velocities is shown numerically using a high predictive simulator of the EMVA system, when considering unknown varying environmental conditions, such as internal friction and external gas pressure forces.     

LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.