Bearing-only visual SLAM for small unmanned aerial vehicles in GPS-denied environments

This paper presents a hierarchical simultaneous localization and mapping(SLAM) system for a small unmanned aerial vehicle(UAV) using the output of an inertial measurement unit(IMU) and the bearing-only observations from an onboard monocular camera.A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match.This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter(EKF) for attitude and velocity estimation.Then,another EKF is employed to estimate the position of the vehicle and the locations of the features in the map.Both simulations and experiments are carried out to test the performance of the proposed system.The result of the comparison with the referential global positioning system/inertial navigation system(GPS/INS) navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.     

未知复杂环境中的无人机平滑飞行路径规划

针对无人机实时路径规划问题,提出了一种基于双层决策的平滑路径规划方法,以弥补现有方法在复杂飞行环境中对路径平滑性优化的不足,增强路径的易跟踪性.本文首先给出路径平滑性度量,然后建模上、下层决策目标、威胁规避与无人机性能约束并引入变长规划时间,进而设计基于双层决策的路径规划模型.规划过程中通过嵌入启发式优化策略来进一步改善路径的全局与局部平滑度,并提高路径搜索效率.大量复杂场景中的仿真及与现有经典方法的对比结果表明:该方法能够实时避开复杂危险区域,规划适合飞行的、较短的平滑路径.     

Target following with motion prediction for unmanned surface vehicle operating in cluttered environments

The capability of following a moving target in an environment with obstacles is required as a basic and necessary function for realizing an autonomous unmanned surface vehicle (USV). Many target following scenarios involve a follower and target vehicles that may have different maneuvering capabilities. Moreover, the follower vehicle may not have prior information about the intended motion of the target boat. This paper presents a trajectory planning and tracking approach for following a differentially constrained target vehicle operating in an obstacle field. The developed approach includes a novel algorithm for computing a desired pose and surge speed in the vicinity of the target boat, jointly defined as a motion goal, and tightly integrates it with trajectory planning and tracking components of the entire system. The trajectory planner generates a dynamically feasible, collision-free trajectory to allow the USV to safely reach the computed motion goal. Trajectory planning needs to be sufficiently fast and yet produce dynamically feasible and short trajectories due to the moving target. This required speeding up the planning by searching for trajectories through a hybrid, pose-position state space using a multi-resolution control action set. The search in the velocity space is decoupled from the search for a trajectory in the pose space. Therefore, the underlying trajectory tracking controller computes desired surge speed for each segment of the trajectory and ensures that the USV maintains it. We have carried out simulation as well as experimental studies to demonstrate the effectiveness of the developed approach.     

非结构化环境中基于拓扑约束的地面无人驾驶路径规划算法

针对非结构化环境地面无人驾驶路径规划过程中路径避障以及多车路径冲突的难题,通过同调以及de Rham上同调对环境中障碍物拓扑信息的精确描述,提出一种拓扑约束下基于A*算法且用时更短的路径规划算法.该算法可实现非结构化环境中多无人车全局路径的拓扑分类,从而为多车的协同规划提供一种新的研究思路.此外,结合C-空间动态广义Voronoi图(GVD)的路径拓扑分离特性,提出一种拓扑约束下可用于多无人车全局路径规划的高效算法-----C-空间-GVD-${h_S     

Antenna array synthesis with clusters of unmanned aerial vehicles

In this paper, we investigate the use of formations of Unmanned Aerial Vehicles (UAVs) as phased antenna arrays. This will help to improve communications with clusters of small unmanned aerial vehicles which are currently constrained by on-board power limitations. The problem of maximizing the power output from the array in the direction of the receiver is posed as an optimization problem which happens to be non-convex; a relaxation of this problem is then solved as a computationally tractable (convex) Second-Order Conic Program (SOCP). The performance obtained by the simplified approach is then tested against rigorous numerical bounds obtained using Semidefinite Programming (SDP) duality theory; these bounds are of independent interest in antenna theory. In order to maintain the objective value close to the optimal when the vehicles deviate from their positions (due to wind gusts, for example), a simple linear control law is proposed. Simulation results are given to show the effectiveness of the proposed approaches.     

Simultaneous sensor selection and routing of unmanned aerial vehicles for complex mission plans

Military reconnaissance missions often employ a set of unmanned aerial vehicles (UAVs) equipped with sensors to gather intelligence information from a set of known targets. UAVs are limited by the number of sensors they can hold; also attaching a sensor adds weight to the aircraft which in turn reduces the flight time available during a mission. The task of optimally assigning sensors to UAVs and routing them through a target field to maximize intelligence gain is a generalization of the team orienteering problem studied in the vehicle routing literature. This work presents a mathematical programming model for simultaneous sensor selection and routing of UAVs, which solves optimally using CPLEX for simple missions. Larger missions required the development of three heuristics, which were augmented by Column Generation. Results from a performance study indicated that the heuristics quickly found good solutions. Column Generation improved the solution in many instances, with minimal impact on overall solution time. The rapid nature of the overall solution approach allows it to be used in other mission planning tasks. A fleet sizing application is discussed as an example of its flexible usage.     

基于集对分析的无人机攻势布雷方案优选

为解决无人机攻势布雷方案优选问题,采用基于联系度的逼近理想解的排序法(TOPSIS)进行求解。在传统评估要素基础上提出无人机综合作战系数来衡量布雷平台影响,并利用联系度刻画备选方案与综合理想方案组成的集对在相对接近程度意义下的同一对立程度。根据特征属性值计算信息熵权,并改进相对确定可能势,以实现无人机攻势布雷方案的综合评估和排序,算例验证了该方法的有效性。     

Neuro-control of unmanned underwater vehicles

Unmanned underwater vehicles (UUVs) typically operate in uncertain and changing environments. Since the dynamics of UUVs are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high-performance control system of a UUV is needed to have the capacities of learning and adaptation to the variations in the UUV's dynamics. This paper presents the utilization of an adaptive neuro-control scheme as a controller for controlling a UUV in six degrees of freedom. No prior offline training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. Asymptotic convergence of the UUV's tracking errors and stability of the presented control system is guaranteed on the basis of the Lyapunov theory. In this paper, neural network architectures based on radial basis functions and multilayer structures have been used to evaluate the performance of the adaptive controller via computer simulation.     

Active sonar-based bottom-following for unmanned underwater vehicles

The problem of high-precision bottom-following in the proximity of the seabed for open-frame unmanned underwater vehicles (UUVs) is addressed in this paper. The suggested approach consists of the integration of a guidance and control system with an active multi-hypothesis extended Kalman filter, able to estimate the motion of the vehicle with respect to the bottom profile. The guidance module is based on the definition of a suitable Lyapunov function associated with the bottom-following task, while the motion controller is a conventional autopilot, performing autoheading, autodepth, and autospeed. The motion of the vehicle is estimated from range and bearing measurements supplied by a high-frequency pencil-beam profiling sonar. Moreover, a general-purpose sensor-based guidance and control system for advanced UUVs, able to manage active sensing-based guidance and motion estimation modules, is presented. An application of the proposed architecture to execute high-precision bottom-following using Romeo, a prototype UUV, developed by the Robotics Dept. of the Istituto Automazione Navale, is described. Experimental results of tests, conducted in a high-diving pool with the vehicle equipped with a sonar profiler, are presented.     

Sonar-based guidance of unmanned underwater vehicles

This paper addresses the problem of the design and coordination of guidance and sonarbased motion estimation algorithms for unmanned underwater vehicles. In the framework of a two-layered hierarchical architecture uncoupling the system's dynamics and kinematics, a couple of guidance laws for approaching a target with the desired orientation and following an environmental feature have been designed with Lyapunov-based techniques. Suitable acoustic-based estimators of the corresponding operational variables have been designed and integrated with the guidance and control system. A finite state machine combined with a suitable interface for event generation allows the coordinated execution of basic guidance and motion estimation tasks to carry out more complex functions. Experimental results of pool trials of a prototype unmanned underwater vehicle executing free-space maneuvering, wall-following tasks and the more complex mission of following the perimeter of the trial pool are reported and discussed.     

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.     

基于DenseNet的无人汽车制动意图识别方法

无人汽车制动意图内部数据由于识别深度增加,会出现过度膨胀现象,导致制动意图数据收集完整度低、识别准确率差。提出基于DenseNet的无人汽车制动示意图识别方法。选择数据深度收集系统,收集无人汽车制动意图内部数据,结合电池保护模型深度分解汽车内部运行过程的能耗,以收集的初始内部数据为标准,整合无人汽车制动意图识别数据,拆分整合数据,防止数据过度膨胀。利用DenseNet的高学习度以及自适应学习性,加权均衡处理内部数据标定函数,设置一组基函数,并选择相应的DenseNet复制内部数据函数,自适应分析复制后的数据,完成制动意图识别。实验结果表明,制动意图数据收集完整度提高15.21%,识别准确率增强了23.68%。     

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.     

Real-time optical SLAM-based mosaicking for unmanned underwater vehicles

This article discusses the possibility of building in real-time a mosaic of the seafloor relying on a simultaneous localization and mapping (SLAM) framework. The goal is to provide an unmanned underwater vehicle with a relatively rough visual map of the seafloor to support basic navigation and context awareness. To achieve that goal, an accurate estimation of the location of the visual landmarks and, in particular, the correct data association when a visual landmark is re-visited by the vehicle are the crucial points. Instead of using a global mosaic, this work uses the combination of a set of local mosaics constructed in the vicinity of the SLAM visual landmarks. The contributions of this article are mainly the use of SURF features, the local mosaics approach and the real-time capability. The use of SURF features allows eliminating false positives in the data association of SLAM visual landmarks. The local mosaics approach is an effective way of correcting the effects of the drift on the mosaic in real time. The main contribution is the real-time capability as it will be seen. The algorithm was tested using a batch of experimental data in typical operating conditions and the results prove the effectiveness of the approach.     

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.     

UGV协同系统研究进展*

无人地面车辆（UGV）在工业自动化、星球探索、灾后救援、智能交通以及军事作战等多任务领域都具有广阔的应用前景。UGV协同系统通过嵌入组织架构、合作策略、交互机制等协同内容,可以达到拓展环境感知范围、提高复杂环境理解适应能力和增强复杂任务工作效能的目的,受到了国内外广泛的关注。从UGV单体/群体体系结构、多重任务协同分配方法以及协同定位、编队、覆盖/探索等几个方面对目前国内外UGV协同工作关键技术进行了总结,给出了一个UGV协同系统的应用实例,并指出了系统发展趋势。     

Unsupervised anomaly detection in unmanned aerial vehicles

A real-time anomaly detection solution indicates a continuous stream of operational and labelled data that must satisfy several resources and latency requirements. Traditional solutions to the problem rely heavily on well-defined features and prior supervised knowledge, where most techniques refer to hand-crafted rules derived from known conditions. While successful in controlled situations, these rules assume that good data is available for them to detect anomalies; indicating that these rules will fail to generalise beyond known scenarios.To investigate these issues, current literature is examined for solutions that can be used to detect known and unknown anomalous instances whilst functioning as an out-of-the-box approach for efficient decision-making. The applicability of the isolation forest is discussed for engineering applications using the Aero-Propulsion System Simulation dataset as a benchmark where it is shown to outperform other unsupervised distance-based approaches. Also, the authors have carried out real-time experiments on an unmanned aerial vehicle to highlight further applications of the method. Finally, some conclusions are drawn concerning its simplicity and robustness in handling diagnostic problems.     

类脑智能技术在无人系统上的应用

随着人工智能与脑科学等前沿技术的迅速发展,无人系统智能化研究正逐渐成为当今世界强国重点关注的战略发展方向,研究与之相关的科学问题具有前瞻性、战略性和带动性.文章首先分析了无人系统的发展需求,提出了面向需求的若干关键问题,包括复杂环境与态势信息的感知与认知问题、整体效能最优的分布式任务决策问题、面向任务需求的路径实时规划问题、考虑高不确定环境的自学习控制问题、应对非预期情况的故障诊断及容错问题以及基于人机接口的人机交互问题;随后,系统阐述了类脑智能技术在解决这些问题上的国内外研究现状;最后,论述了无人系统类脑智能化发展中依然存在的问题及未来发展趋势.     

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.     

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.