Vision-based target tracking with a small UAV: Optimization-based control strategies

This paper considers the problem of a small, fixed-wing UAV equipped with a gimbaled camera autonomously tracking an unpredictable moving ground vehicle. Thus, the UAV must maintain close proximity to the ground target and simultaneously keep the target in its camera׳s visibility region. To achieve this objective robustly, two novel optimization-based control strategies are developed. The first assumes an evasive target motion while the second assumes a stochastic target motion. The resulting optimal control policies have been successfully flight tested, thereby demonstrating the efficacy of both approaches in a real-world implementation and highlighting the advantages of one approach over the other.     

Design and implementation of an autonomous flight control law for a UAV helicopter

In this paper, we present the design and implementation of an autonomous flight control law for a small-scale unmanned aerial vehicle (UAV) helicopter. The approach is decentralized in nature by incorporating a newly developed nonlinear control technique, namely the composite nonlinear feedback control, together with dynamic inversion. The overall control law consists of three hierarchical layers, namely, the kernel control, command generator and flight scheduling, and is implemented and verified in flight tests on the actual UAV helicopter. The flight test results demonstrate that the UAV helicopter is capable of carrying out complicated flight missions autonomously.     

Min–max time consensus tracking on a multi-quadrotor testbed

In this work, min–max time consensus tracking is implemented on a multi-quadrotor testbed. The trajectory of a leader quadrotor is generated manually. The remaining quadrotors converge onto this reference trajectory in min–max time using a local feedback control strategy which is known to be globally optimal. Further, the effect of finite communication/measurement rate on consensus tracking is analysed. The bounds on the deviations of the trajectories due to finite communication/measurement rate are characterized. The theoretical claims made are verified through experiments.     

Outdoor cooperative flight using decentralized consensus algorithm and a guaranteed real-time communication protocol

This paper describes the implementation of a decentralized consensus law with theoretically provable convergence properties on a multi-agent testbed comprising of quadrotors. It is shown that for small roll and pitch angles and well-tuned control loops, the quadrotor dynamics can be approximated as a pair of double integrators. Several experiments are carried out in an outdoor environment for validation of the consensus law which is based on double integrator dynamics. For any arbitrary initial positions of the quadrotors, the consensus law is able to drive them to an autonomously decided common point, given that the communication graph is connected at each instant of time. The resulting experimental trajectories and the consensus point matches with theoretical predictions. For guaranteeing real-time reliability required for such coordinated motion, a novel synchronized, time-slotted, scalable and fully airborne communication protocol is proposed. The protocol avoids data collisions and ensures real-time, reliable communication between agents. It can also address changing communication graph topologies, temporary link-breaks and additions. Using this underlying protocol, the quadrotors attain consensus for static and dynamic communication graphs. Experiments to observe the effect of communication rate on consensus performance are also conducted.     

Fully autonomous micro air vehicle flight and landing on a moving target using visual–inertial estimation and model‐predictive control

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) held in spring 2017 was a very successful competition well attended by teams from all over the world. One of the challenges (Challenge 1) required an aerial robot to detect, follow, and land on a moving target in a fully autonomous fashion. In this paper, we present the hardware components of the micro air vehicle (MAV) we built with off the self components alongside the designed algorithms that were developed for the purposes of the competition. We tackle the challenge of landing on a moving target by adopting a generic approach, rather than following one that is tailored to the MBZIRC Challenge 1 setup, enabling easy adaptation to a wider range of applications and targets, even indoors, since we do not rely on availability of global positioning system. We evaluate our system in an uncontrolled outdoor environment where our MAV successfully and consistently lands on a target moving at a speed of up to 5.0 m/s.     

Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives

This paper presents a review on the development and application of model predictive control (MPC) for autonomous intelligent mechatronic systems (AIMS). Starting from the conceptual analysis of “mechatronics”, we analyze the characteristics and control system design requirements of AIMS. In order to fulfill the design requirements, we propose to develop a unified MPC framework for AIMS. The main MPC schemes, covering MPC basics, robust MPC, distributed MPC, Lyapunov-based MPC, event-based MPC, network-based MPC, switched MPC, fast MPC, are reviewed with an attempt to document some of the key achievements in the past decades. Furthermore, we provide the review and analysis of MPC applications to three types of mechatronic systems, including unmanned aerial vehicles (UAVs), autonomous marine vehicles (AMVs), and autonomous ground robots (AGRs). Some promising research directions and concluding remarks are presented.     

基于光流的四旋翼直升机鲁棒自主着陆控制

针对小型四旋翼无人机自主着陆问题,提出了一种基于光流的高度估计方法和基于信号补偿的高度鲁棒控制器设计方法.首先从通用光流运动模型出发,采用奇异值分解方法求解无人机线速度和深度的比值,通过对垂直速度和高度比值积分获得高度数据.其次,将考虑地效影响和其它不确定性的高度通道非线性模型分解为标称线性模型和等效扰动两部分,并设计基于信号补偿的高度鲁棒控制器,该控制器由标称控制器和鲁棒补偿器组成,其中标称控制器使得标称闭环系统达到期望的高度跟踪特性,鲁棒补偿器用于抑制等效扰动的影响.最后从理论上证明了该控制器可以保证高度跟踪误差在有限时间内收敛至指定的原点邻域内.四旋翼无人机自主着陆的实验结果验证了所提出的基于光流的高度估计和鲁棒控制方法的有效性.     

Extremum seeking for moderately unstable systems and for autonomous vehicle target tracking without position measurements

We remove the long standing restriction that plant dynamics in extremum seeking control must be stable and provide an extension that allows single integrators, double integrators, and moderately unstable single poles. An application of the result for single and double integrators is in control of autonomous vehicles. Extremum seeking is used for finding a source of a signal (chemical, electromagnetic, etc.) whose strength decays with the distance. This is achieved without the measurement of the position vector and using only the measurement of the scalar signal.     

A vision-based strategy for autonomous aerial refueling tasks

Autonomous aerial refueling is a key enabling technology for both manned and unmanned aircraft where extended flight duration or range are required. The results presented within this paper offer one potential vision-based sensing solution, together with a unique test environment. A hierarchical visual tracking algorithm based on direct methods is proposed and developed for the purposes of tracking a drogue during the capture stage of autonomous aerial refueling, and of estimating its 3D position. Intended to be applied in real time to a video stream from a single monocular camera mounted on the receiver aircraft, the algorithm is shown to be highly robust, and capable of tracking large, rapid drogue motions within the frame of reference. The proposed strategy has been tested using a complex robotic testbed and with actual flight hardware consisting of a full size probe and drogue. Results show that the vision tracking algorithm can detect and track the drogue at real-time frame rates of more than thirty frames per second, obtaining a robust position estimation even with strong motions and multiple occlusions of the drogue.     

A robust online path planning approach in cluttered environments for micro rotorcraft drones

We present in this paper a robust online path planning method, which allows a micro rotorcraft drone to fly safely in GPS-denied and obstacle-strewn environments with limited onboard computational power. The approach is based on an efficiently managed grid map and a closed-form solution to the two point boundary value problem (TPBVP). The grid map assists trajectory evaluation whereas the solution to the TPBVP generates smooth trajectories. Finally, a top-level trajectory switching algorithm is utilized to minimize the computational cost. Advantages of the proposed approach include its conservation of computational resource, robustness of trajectory generation and agility of reaction to unknown environment. The result has been realized on actual drones platforms and successfully demonstrated in real flight tests. The video of flight tests can be found at: http://uav.ece.nus.edu.sg/robust-online-path-planning-Lai2015.html.     

Design and Implementation of a Flight Control System for an Unmanned Rotorcraft using RPT Control Approach

In this paper, we apply a so‐called robust and perfect tracking (RPT) control technique to the design and implementation of the flight control system of a miniature unmanned rotorcraft, named HeLion. To make the presented work self‐contained, we will first outline some background knowledge, including mainly the nonlinear flight dynamics model and the inner‐loop flight control system design. Next, the highlight of this paper, that is, the outer‐loop flight control system design procedure using RPT control technique, will be detailed. Generally speaking, RPT control technique aims to design a controller such that (i) the resulting closed‐loop system is asymptotically stable, and (ii) the controlled output almost perfectly tracks a given reference signal in the presence of any initial conditions and external disturbances. Since it makes use of all possible information including the system measurement output and the command reference signal together with all its derivatives (if available) for control, RPT control technique is particularly useful for the outer‐loop layer of an unmanned aircraft. Both simulation and flight‐test results will be presented and analyzed at the end of this paper, and the efficiency of the RPT control approach will be evaluated comprehensively.     

Effect of time pressure and target uncertainty on human operator performance and workload for autonomous unmanned aerial system

Autonomous unmanned aircraft systems (UAS) are being utilized at an increasing rate for a number of military applications. The role of a human operator differs from that of a pilot in a manned aircraft, and this new role creates a need for a shift in interface and task design in order to take advantage of the full potential of these systems. This study examined the effects of time pressure and target uncertainty on autonomous unmanned aerial vehicle operator task performance and workload. A 2 × 2 within subjects experiment design was conducted using Multi-Modal Immersive Intelligent Interface for Remote Operation (MIIIRO) software. The primary task was image identification, and secondary tasks consisted of responding to events encountered in typical UAS operations. Time pressure was found to produce a significant difference in subjective workload ratings as well as secondary task performance scores, while target uncertainty was found to produce a significant difference in the primary task performance scores. Interaction effects were also found for primary tasks and two of the secondary tasks. This study has contributed to the knowledge of UAS operation, and the factors which may influence performance and workload within the UAS operator. Performance and workload effects were shown to be elicited by time pressure. Relevance to industry: The research findings from this study will help the UAS community in designing human computer interface and enable appropriate business decisions for staffing and training, to improve system performance and reduce the workload.     

Heuristic based evolution for the coordination of autonomous vehicles in the absence of speed lanes

The current state of the art in the planning and coordination of autonomous vehicles is based upon the presence of speed lanes. In a traffic scenario where there is a large diversity between vehicles the removal of speed lanes can generate a significantly higher traffic bandwidth. Vehicle navigation in such unorganized traffic is considered. An evolutionary based trajectory planning technique has the advantages of making driving efficient and safe, however it also has to surpass the hurdle of computational cost. In this paper, we propose a real time genetic algorithm with Bezier curves for trajectory planning. The main contribution is the integration of vehicle following and overtaking behaviour for general traffic as heuristics for the coordination between vehicles. The resultant coordination strategy is fast and near-optimal. As the vehicles move, uncertainties may arise which are constantly adapted to, and may even lead to either the cancellation of an overtaking procedure or the initiation of one. Higher level planning is performed by Dijkstra's algorithm which indicates the route to be followed by the vehicle in a road network. Re-planning is carried out when a road blockage or obstacle is detected. Experimental results confirm the success of the algorithm subject to optimal high and low-level planning, re-planning and overtaking.     

Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art

In recent years, since researchers began to study on Unmanned Aerial Vehicles (UAVs), UAVs have been integrated into today's everyday life, including civilian area and military area. Many researchers have tried to make use of UAVs as an ideal platform for inspection, delivery, surveillance, and so on. In particular, machine learning has been applied to UAVs for autonomous flight that enables UAVs do designated task more efficiently. In this paper, we review the history and the classification of machine learning, and discuss the state-of-the-art machine learning that has been applied to UAVs for autonomous flight. We provide control strategies including parameter tuning, adaptive control for uncertain environment, and real-time path planning, and object recognition that have been described in the literature.     

A probabilistic approach for designing nonlinear optimal robust tracking controllers for unmanned aerial vehicles

In this study, we propose a probabilistic approach for designing nonlinear optimal robust tracking controllers for unmanned aerial vehicles. The controller design is formulated in terms of a multi-objective optimization problem that is solved by using a bio-inspired optimization algorithm, offering high likelihood of finding an optimal or near-optimal global solution. The process of tuning the controller minimizes differences between system outputs and optimal specifications given in terms of rising time, overshoot and steady-state error, and the controller succeed in fitting the performance requirements even considering parametric uncertainties and the nonlinearities of the aircraft. The stability of the controller is proved for the nominal case and its robustness is carefully verified by means of Monte Carlo simulations.     

Trajectory generation for autonomous soaring UAS

As unmanned aerial vehicles are expected to do more and more advanced tasks, improved range and persistence is required. This paper presents a method of using shallow layer cumulus convection to extend the range and duration of small unmanned aerial vehicles. A simulation model of an X-models XCalubur electric motor-glider is used in combination with a refined 4D parametric thermal model to simulate soaring flight. The parametric thermal model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation.     

H-Infinity Static Output-feedback Control for Rotorcraft

The problem of stabilization of an autonomous rotorcraft platform in a hover configuration subject to external disturbances is addressed. Necessary and sufficient conditions are presented for static output-feedback control of linear time-invariant systems using the H-Infinity approach. Simplified conditions are given which only require the solution of two coupled matrix design equations. This paper also proposes a numerically efficient solution algorithm for the coupled design equations to determine the output-feedback gain. A major contribution is that an initial stabilizing gain is not needed. The efficacy of the control law and the disturbance accommodation properties are shown on a rotorcraft design example. The helicopter dynamics do not decouple as in the fixed-wing aircraft case, so that the design of helicopter flight controllers with a desirable intuitive structure is not straightforward. In this paper an output feedback approach is given that allows one to selectively close prescribed multivariable feedback loops using a reduced set of the states. Shaping filters are added that improve performance and yield guaranteed robustness and speed of response. This gives direct control over the design procedure and performance. Accurate identification of the System parameters is a challenging task for rotorcraft control, addition of loop shaping facilitates implementation engineers to counteract unmodeled high frequency dynamics. The net result yields control structures that have been historically accepted in the flight control community.     

舰载无人机着舰轨迹跟踪鲁棒控制器设计

为确保舰载无人机着舰阶段精确地跟踪下滑轨迹,并且在稳态性能基础上具有更好的瞬态性能,本文在新的等效误差模型的基础上提出了受限指令预设性能控制律设计方法.首先考虑了舵面受限和角速率受限等实际工程问题,引入受限指令滤波方法,然后综合考虑建模误差和外界扰动,引入了连续的双曲正切函数对饱和函数进行近似,并设计了自适应律对模型未知参数进行估计,最后引入了预设性能方法对着舰的瞬态性能进行了重点分析.通过理论推导和仿真验证,证明了本文提出的方法在复杂着舰环境下有较好的瞬态性能和较好的鲁棒性.