A lightweight autonomous MAV for indoor search and rescue

Micro Aerial Vehicles (MAVs) have great potentials to be applied for indoor search and rescue missions. In this paper, we propose a modular lightweight design of an autonomous MAV with integrated hardware and software. The MAV is equipped with the 2D laser scanner, camera, mission computer and flight controller, running all the computation onboard in real time. The onboard perception system includes a laser‐based SLAM module and a custom‐designed visual detection module. A dual Kalman filter design provides robust state estimation by multiple sensor fusion. Specifically, the fusion module provides robust altitude measurement in the circumstance of surface changing. In addition, indoor‐outdoor transition is explicitly handled by the fusion module. In order to efficiently navigate through obstacles and adapt to multiple tasks, a task tree‐based mission planning method is seamlessly integrated with path planning and control modules. The MAV is capable of searching and rescuing victims from unknown indoor environments effectively. It was validated by our award‐winning performance at the 2017 International Micro Air Vehicle Competition (IMAV 2017), held in Toulouse, France. The performance video is available on https://youtu.be/8H19ppS_VXM .     

A multilevel sampled‐data approach for resilient navigation and control of autonomous systems

Autonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high‐level reference commands are limited by a saturation function, whereas the low‐level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple‐input–multiple‐output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output‐feedback adaptive controller is developed as the low‐level controller. The sampled‐data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero‐dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed‐loop system with command saturation and multirate adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed‐wing drone under failures/attacks are provided to validate the theoretical findings.     

适应于自动驾驶的计算结构与平台综述

信息与通信技术领域的不断发展,推动了自动驾驶汽车的巨大进步。自动驾驶汽车有效解决了用户对出行日益提高的安全、便捷、舒适、高效等个性化需求,有着巨大的商业前景和应用价值。首先从自动驾驶的定义出发,对自动驾驶汽车计算任务进行了分类总结,阐述了自动驾驶汽车集移动端、边缘节点和云端一体的计算结构。然后分类讨论了目前自动驾驶汽车移动端计算平台,呈现出异构多核的特点,并对当前主流的底层计算平台进行了对比分析。最后,从性能与成本和信息安全两个方面总结目前自动驾驶汽车计算结构和平台遇到的问题与挑战。     

Subspace Clustering for Situation Assessment in Aquatic Drones: A Sensitivity Analysis for State-Model Improvement

Abstract

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.     

隧道安全预警机器人自主导航方法研究

针对隧道施工环境,设计了一款隧道安全预警机器人,并重点研究其导航系统实现;机器人搭载检测仪器与分析主机,集数据采集、分析、预警于一体,实现隧道巡检的自动化和智能化;分别利用Cartographer算法和自适应蒙特卡洛算法(AMCL)融合雷达、里程计、IMU的数据,实现环境地图的创建与机器人实时定位;采用A * 算法和DWA算法实现机器人的全局和局部路径规划,并结合ROS操作系统对其进行实验验证;实验结果表明:在导航定位时,机器人位置误差和航向误差分别小于20 cm和5.5.,能满足实际工程需要,完成自动巡检任务.     

工程范式背景下毕业设计引导策略

基于工程范式提出毕业设计的引导策略,毕业设计选题侧重学生创新能力的培养,并结合学生职业规划激发学习兴趣。企业通过校企合作吸引和培养人才,是校企合作联合培养得到落实的关键;而企业培训高校青年教师则是更为高效的合作形式。毕业设计采用自主学习模式是师生的最佳选择,有利于学生终身学习能力的形成。     

A concurrent control architecture for autonomous mobile robots using Asynchronous Production Systems

The quest for efficient real-time response by autonomous robots in hazardous environments necessitates not only fast computational schemes in expert systems but also requires insights on high performance data structures and concurrent algorithms that lead to elegant problem-solving methods in the AI discipline. towards this objective, this paper presents an Asynchronous Production System (APS) of architecture capable of monitoring and processing real-time information.     

基于空间交汇测量技术的悬臂式掘进机位姿自主测量方法

根据深部危险煤层无人化开采的需求,要实现无人环境下对悬臂式掘进机位姿的高精度测量。提出一种基于空间交汇测量技术的悬臂式掘进机自主位姿测量方法,由悬臂式掘进机搭载激光发射器并发射旋转激光平面,在其后方安装位置固定的激光接收器并通过激光平面获取激光发射器相对于其自身的方位,从而得到悬臂式掘进机相对于由激光接收器确定的巷道坐标系的位姿状态。构建悬臂式掘进机位姿测量的数学模型,运用仿真软件对该模型进行仿真并分析位姿测量精度。仿真表明：在激光发射器与激光接收器相距25 m时悬臂式掘进机定位点的最大测量误差在X轴,测量误差为0.082 3 m;姿态角的最大测量误差为横滚角,测量误差为2.018 4°。基本满足目前煤矿综掘工作面对悬臂式掘进机位姿测量精度的要求。     

多关节自主游动机器鱼的设计与控制

为研究机器鱼设计过程中的关键问题(如密封、配重)以及机器鱼控制中的难点(如逼真的模拟鱼的游动),设计了四关节且可以自主游动的鲹科机器鱼研究平台,展示了机器鱼机械结构和控制系统的设计过程。水下实验证明此机器鱼平台可行而有效,能够真实地模拟真鱼的游动姿态和行为,可用于后续研究,如提高机器鱼的游动效率、优化机器鱼的控制算法等。     

地下铲运机多模式自主行驶控制方法研究

针对井下复杂多变的巷道环境条件和地下铲运机的车身结构特点,将地下铲运机目标路径规划及偏差计算结合起来,并划分为四种计算控制模式,即扫描光束三角计算偏差、扫描光束对称计算偏差、人工示教和轨迹偏差推算等控制模式。通过在不同的井下巷道环境条件下,运用不同的偏差计算控制模式或将其组合使用,在减少计算量和控制复杂性的同时,取得地下铲运机较理想的自主行驶控制效果。