旋翼空中机器人系统架构及设计

设计并验证了某型旋翼空中机器人的系统架构。整个空中机器人系统由直升机和地面站两部分组成。直升机是空中机器人的主体,可以自主飞行并完成指定任务。地面站用于监控无人直升机的飞行,并实现人机交互等多项功能。此外,地面站还可通过视觉导航系统引导直升机的自主着陆。直升机与地面站之间通过指令数字链路和视频模拟链路进行信息交互和实时通讯。经实际飞行验证,该空中机器人系统具有鲁棒和实时的特点,能实现直升机自主飞行和自主起降功能。     

崎岖地表上的旋翼无人机自主安全降落系统

针对旋翼无人机全自主作业的需求,构建了崎岖地表上的旋翼无人机自主安全降落系统．该系统通过机载实时运算自动分析落区地形,寻找可行落点并实施自动降落．系统以低成本的立体RGB-D相机作为深度传感设备,利用截断符号距离函数（TSDF）对着陆区地形进行实时3维建模,生成低噪的落区地形深度图像,并设计了一种适应起落机构形状的实时精细落点搜索方法,最后使用级联PID（比例－积分－微分）控制器控制无人机实施安全降落．系统基于大疆M100无人机平台实现,定制了仿真器进行算法调试,并最终在实际的崎岖地表上实现了自主安全降落．本文工作可为旋翼无人机紧急降落、物流运输或者灾后搜救提供有效安全的解决方案．     

Autonomous takeoff and landing control for a prototype unmanned helicopter

In this paper, an autonomous takeoff and landing control strategy is designed and implemented for a prototype coaxial unmanned helicopter with ducted fan configuration. The control strategy is designed such that longitudinal and lateral controls use ground forces, attitude and drifting feedbacks. Vertical control employs takeoff and landing decision and vertical velocity control is based on altitude tracking. Ground forces feedback is used to balance longitudinal forces and moments during liftoff effectively cancelling all ground forces. Attitude and drifting feedbacks are used to balance the longitudinal and lateral movements of the helicopter during takeoff and landing. The flight control strategy is successfully verified during flight tests.     

Competent vision and navigation systems

In this article, we describe how flying insects use vision for guidance, especially in the contexts of regulating flight speed, negotiating narrow gaps, avoiding obstacles, and performing smooth landings. We show that many of these maneuvers, which were traditionally believed to involve relatively complex and high-level perception, can be achieved through the use of low-level cues and relatively simple computation. We also describe tests of the effectiveness of some of these strategies for autonomous guidance of small-scale terrestrial and aerial vehicles in the contexts of corridor navigation, altitude control, and terrain following and landing. We also describe a novel, mirror- based imaging system that is tailored for these tasks and facilitates the requisite visual computations.     

Weaver: Hexapod robot for autonomous navigation on unstructured terrain

Legged robots are an efficient alternative for navigation in challenging terrain. In this paper we describe Weaver, a six‐legged robot that is designed to perform autonomous navigation in unstructured terrain. It uses stereo vision and proprioceptive sensing based terrain perception for adaptive control while using visual‐inertial odometry for autonomous waypoint‐based navigation. Terrain perception generates a minimal representation of the traversed environment in terms of roughness and step height. This reduces the complexity of the terrain model significantly, enabling the robot to feed back information about the environment into its controller. Furthermore, we combine exteroceptive and proprioceptive sensing to enhance the terrain perception capabilities, especially in situations in which the stereo camera is not able to generate an accurate representation of the environment. The adaptation approach described also exploits the unique properties of legged robots by adapting the virtual stiffness, stride frequency, and stride height. Weaver's unique leg design with five joints per leg improves locomotion on high gradient slopes, and this novel configuration is further analyzed. Using these approaches, we present an experimental evaluation of this fully self‐contained hexapod performing autonomous navigation on a multiterrain testbed and in outdoor terrain.     

Ambler: an autonomous rover for planetary exploration

The authors are building a prototype legged rover, called the Ambler (loosely an acronym for autonomous mobile exploration robot) and testing it on full-scale, rugged terrain of the sort that might be encountered on the Martian surface. They present an overview of their research program, focusing on locomotion, perception, planning, and control. They summarize some of the most important goals and requirements of a rover design and describe how locomotion, perception, and planning systems can satisfy these requirements. Since the program is relatively young (one year old at the time of writing) they identify issues and approaches and describe work in progress rather than report results. It is expected that many of the technologies developed will be applicable to other planetary bodies and to terrestrial concerns such as hazardous waste assessment and remediation, ocean floor exploration, and mining     

Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots

This paper describes the use of a genetic algorithm (GA) for the problem of offline point-to-point autonomous mobile robot path planning. The problem consists of generating “valid” paths or trajectories, for an Holonomic Robot to use to move from a starting position to a destination across a flat map of a terrain, represented by a two-dimensional grid, with obstacles and dangerous ground that the Robot must evade. This means that the GA optimizes possible paths based on two criteria: length and difficulty. First, we decided to use a conventional GA to evaluate its ability to solve this problem (using only one criteria for optimization). Due to the fact that we also wanted to optimize paths under two criteria or objectives, then we extended the conventional GA to implement the ideas of Pareto optimality, making it a multi-objective genetic algorithm (MOGA). We describe useful performance measures and simulation results of the conventional GA and of the MOGA that show that both types of GAs are effective tools for solving the point-to-point path-planning problem.     

Fusion of Data and Knowledge for Safe UAV Landing

Autonomous Unmanned Aerial Vehicles (UAVs) have the potential to significantly improve current working practices for a variety of applications including aerial surveillance and search-and-rescue. However before UAVs can be widely integrated into civilian airspace there are a number of technical challenges which must be overcome including provision of an autonomous method of landing which would be executed in the event of an emergency. A fundamental component of autonomous landing is safe landing zone detection of which terrain classification is a major constituent. Presented in this paper is an extension of the Multi-Modal Expectation Maximization algorithm which combines data in the form of multiple images of the same scene, with knowledge in the form of historic training data and Ordnance Survey map information to compute updated class parameters. These updated parameters are subsequently used to classify the terrain of an area based on the pixel data contained within the images. An image''s contribution to the classification of an area is then apportioned according to its coverage of that area. Preliminary results are presented based on aerial imagery of the Antrim Plateau region in Northern Ireland which indicates potential in the approach used.     

Hovering control of model helicopter by vision

In this paper, we introduce an autonomous flying model helicopter with a vision control system. A feature of the helicopter is that autonomous hovering is realized by a vision control system. Owing to this vision control system, the model helicopter is able to take off, land, and hover without any human assistance. The vision control sstem is composed of a CCD camera mounted on the helicopter and an image processor on the ground. We first introduce the configuration of the helicopter system, which has a vision sensor, a clinometer, and an azimuth sensor. To determine the 3-D position and posture of helicopter, a technique of image recognition using a monocular image is used. Finally, we give an experiment result which we obtained in a hovering test with the vision control system. This result shows the effectiveness of the vision control system in the model helicopter.     

小型无人直升机着陆高度单目视觉测量方法

针对小型无人直升机自主降落过程中姿态发生变化和非差分GPS高程精度不高的情况,研究了一种基于单目视觉的小型无人直升机离地高度测量方法,用于提高自主降落的品质。在机体姿态角已知的情况下,识别提取降落图标中相互平行的特征直线段,并根据机载摄像头投影模型,实时求解机体降落过程中的离地高度。在测量系统上进行了实体实验,验证了所提出的方法,实验结果表明:该方法特征识别率高,在机体平面与地面存在倾角时,输出结果的实时性与准确性亦均能满足小型无人直升机自主降落要求。     

Modeling curiosity in a mobile robot for long-term autonomous exploration and monitoring

This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility. We use a realtime topic modeling technique to build a semantic perception model of the environment, using which, we plan a path through the locations in the world with high semantic information content. The life-long learning behavior of the proposed perception model makes it suitable for long-term exploration missions. We validate the approach using simulated exploration experiments using aerial and underwater data, and demonstrate an implementation on the Aqua underwater robot in a variety of scenarios. We find that the proposed exploration paths that are biased towards locations with high topic perplexity, produce better terrain models with high discriminative power. Moreover, we show that the proposed algorithm implemented on Aqua robot is able to do tasks such as coral reef inspection, diver following, and sea floor exploration, without any prior training or preparation.     

Autonomous robot navigation in outdoor cluttered pedestrian walkways

This paper describes an implementation of a mobile robot system for autonomous navigation in outdoor concurred walkways. The task was to navigate through nonmodified pedestrian paths with people and bicycles passing by. The robot has multiple redundant sensors, which include wheel encoders, an inertial measurement unit, a differential global positioning system, and four laser scanner sensors. All the computation was done on a single laptop computer. A previously constructed map containing waypoints and landmarks for position correction is given to the robot. The robot system's perception, road extraction, and motion planning are detailed. The system was used and tested in a 1‐km autonomous robot navigation challenge held in the City of Tsukuba, Japan, named “Tsukuba Challenge 2007.” The proposed approach proved to be robust for outdoor navigation in cluttered and crowded walkways, first on campus paths and then running the challenge course multiple times between trials and the challenge final. The paper reports experimental results and overall performance of the system. Finally the lessons learned are discussed. The main contribution of this work is the report of a system integration approach for autonomous outdoor navigation and its evaluation. © 2009 Wiley Periodicals, Inc.     

Driving on Point Clouds: Motion Planning,Trajectory Optimization,and Terrain Assessment in Generic Nonplanar Environments

We present a practical approach to global motion planning and terrain assessment for ground robots in generic three‐dimensional (3D) environments, including rough outdoor terrain, multilevel facilities, and more complex geometries. Our method computes optimized six‐dimensional trajectories compliant with curvature and continuity constraints directly on unordered point cloud maps, omitting any kind of explicit surface reconstruction, discretization, or topology extraction. We assess terrain geometry and traversability on demand during motion planning, by fitting robot‐sized planar patches to the map and analyzing the local distribution of map points. Our motion planning approach consists of sampling‐based initial trajectory generation, followed by precise local optimization according to a custom cost measure, using a novel, constraint‐aware trajectory optimization paradigm. We embed these methods in a complete autonomous navigation system based on localization and mapping by means of a 3D laser scanner and iterative closest point matching, suitable for both static and dynamic environments. The performance of the planning and terrain assessment algorithms is evaluated in offline experiments using recorded and simulated sensor data. Finally, we present the results of navigation experiments in three different environments—rough outdoor terrain, a two‐level parking garage, and a dynamic environment, demonstrating how the proposed methods enable autonomous navigation in complex 3D terrain.     

Intuitive 3D Maps for MAV Terrain Exploration and Obstacle Avoidance

Recent development showed that Micro Aerial Vehicles (MAVs) are nowadays capable of autonomously take off at one point and land at another using only one single camera as exteroceptive sensor. During the flight and landing phase the MAV and user have, however, little knowledge about the whole terrain and potential obstacles. In this paper we show a new solution for a real-time dense 3D terrain reconstruction. This can be used for efficient unmanned MAV terrain exploration and yields a solid base for standard autonomous obstacle avoidance algorithms and path planners. Our approach is based on a textured 3D mesh on sparse 3D point features of the scene. We use the same feature points to localize and control the vehicle in the 3D space as we do for building the 3D terrain reconstruction mesh. This enables us to reconstruct the terrain without significant additional cost and thus in real-time. Experiments show that the MAV is easily guided through an unknown, GPS denied environment. Obstacles are recognized in the iteratively built 3D terrain reconstruction and are thus well avoided.     

Vision-Based Landing of Light Weight Unmanned Helicopters on a Smart Landing Platform

This paper presents a simple and efficient solution to vision guided autonomous landing of a light-weight (<150 Kg) unmanned helicopter on a smart landing platform, called ISLANDS??Intelligent Self-Leveling and Nodal Docking System. The advantage of ISLANDS is that it may allow the helicopter upon docking to recharge its batteries or refuel, thus, indirectly increasing endurance and flight range. In order for the helicopter to dock with ISLANDS, an on-board ??vision module?? coupled with the helicopter attitude controller is developed. This ??vision module?? detects the location and orientation of ISLANDS and feeds back information to the helicopter attitude controller, which commands the helicopter to descent onto the landing platform at a desired orientation and speed. The Scale Invariant Feature Transform (SIFT) is used for automatic detection of the landing platform based on images captured by a single camera mounted on the helicopter. The detected SIFT features are used to estimate the 3-D orientation of the platform relative to the helicopter using Homography and RANSAC techniques. The focus of this paper is on the vision-guided landing technique in a predefined orientation and not on controller details, which may be found in Shim et al. (1998).     

A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural Landmarks

In this paper a vision-based approach for guidance and safe landing of an Unmanned Aerial Vehicle (UAV) is proposed. The UAV is required to navigate from an initial to a final position in a partially known environment. The guidance system allows a remote user to define target areas from a high resolution aerial or satellite image to determine either the waypoints of the navigation trajectory or the landing area. A feature-based image-matching algorithm finds the natural landmarks and gives feedbacks to an onboard, hierarchical, behaviour-based control system for autonomous navigation and landing. Two algorithms for safe landing area detection are also proposed, based on a feature optical flow analysis. The main novelty is in the vision-based architecture, extensively tested on a helicopter, which, in particular, does not require any artificial landmark (e.g., helipad). Results show the appropriateness of the vision-based approach, which is robust to occlusions and light variations.     

Side-to-side 3D coverage path planning approach for agricultural robots to minimize skip/overlap areas between swaths

Automated path planning is an important tool for the automation and optimization of field operations. It can provide the waypoints required for guidance, navigation and control of agricultural robots and autonomous tractors throughout the execution of these field operations. Typical field operations are repetitively required nearly every cropping season and therefore it should be carried out in a manner that maximizes the yield and minimizes operational cost, time and environmental impact taking into account the topographic land features. Current 3D terrain field coverage path planning algorithms are simply 2D coverage path planning projected into 3D through field terrain represented by the field’s Digital Elevation Model (DEM). When projecting 2D coverage plan into its 3D counterpart, the actual distance between adjacent paths on the topographic surface either increases or decreases, and consequently there might be skips or overlaps between adjacent paths on the slopes. In addition, when the machine rolls on slopes the effective width of the implement decreases by a similar amount to double this error and complicates the problem. Skips and overlaps can lead to an inefficient use of land and resources. In this paper, a numerical approach to estimate the total skip/overlap areas is developed and applied to determine the optimum-driving angle that minimizes this impact. Also, a novel side-to-side 3D coverage path planning approach, which ensures zero skips/overlaps regardless of the topographical nature of the field terrain, is developed. The approaches developed in this paper are tested and validated using a hypothetical test field of a tailored terrain and a real experimental field of uneven terrain nature. The proposed approaches illustrated that a significant percentage of uncovered area could be saved if appropriate driving angle is chosen and if a side-to-side 3D coverage is used.     

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

While impressive progress has recently been made with autonomous vehicles, both indoors and on streets, autonomous localization and navigation in less constrained and more dynamic environments, such as outdoor pedestrian and bicycle‐friendly sites, remains a challenging problem. We describe a new approach that utilizes several visual perception modules—place recognition, landmark recognition, and road lane detection—supplemented by proximity cues from a planar laser range finder for obstacle avoidance. At the core of our system is a new hybrid topological/grid‐occupancy map that integrates the outputs from all perceptual modules, despite different latencies and time scales. Our approach allows for real‐time performance through a combination of fast but shallow processing modules that update the map's state while slower but more discriminating modules are still computing. We validated our system using a ground vehicle that autonomously traversed three outdoor routes several times, each 400 m or longer, on a university campus. The routes featured different road types, environmental hazards, moving pedestrians, and service vehicles. In total, the robot logged over 10 km of successful recorded experiments, driving within a median of 1.37 m laterally of the center of the road, and localizing within 0.97 m (median) longitudinally of its true location along the route.     

基于仿生双目的无人旋翼机自主着陆方法

基于仿生双目理论提出一种仿双眼异向运动的变视轴夹角双目视觉系统,通过对实时采集的目标图像的处理,研究其双目摄像机夹角的在线自标定,实现获取无人旋翼机的实时高度信息。实验结果表明,该方法获得的光轴夹角信息相对误差小于5%,能准确控制双目摄像机的转动,避免由于双目视觉盲区所造成的着陆误差,系统所标定出的旋翼机高度信息也为其安全着陆提供了有力保障。     

Approaches for a tether-guided landing of an autonomous helicopter

In this paper, we address the design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea. A tether is used for landing and securing a helicopter to the deck of the ship in rough weather. A detailed nonlinear dynamic model for the helicopter is used. This model is underactuated, where the rotational motion couples into the translation. This property is used to design controllers which separate the time scales of rotation and translation. It is shown that the tether tension can be used to couple the translation of the helicopter to the rotation. Two controllers are proposed in this paper. In the first, the rotation time scale is chosen much shorter than the translation, and the rotation reference signals are created to achieve a desired controlled behavior of the translation. In the second, due to coupling of the translation of the helicopter to the rotation through the tether, the translation reference rates are created to achieve a desired controlled behavior of the attitude and altitude. Controller A is proposed for use when the helicopter is far away from the goal, while Controller B is for the case when the helicopter is close to the ship. The proposed control schemes are proved to be robust to the tracking error of its internal loop and results in local exponential stability. The performance of the control system is demonstrated by computer simulations. Currently, work is in progress to implement the algorithm using an instrumented model of a helicopter with a tether.