Towards discrimination of challenging conditions for UGVs with visual and infrared sensors

This work aims to contribute to reliability and integrity in perceptual systems of autonomous ground vehicles. Information theoretic based metrics to evaluate the quality of sensor data are proposed and applied to visual and infrared camera images. The contribution of the proposed metrics to the discrimination of challenging conditions is discussed and illustrated with the presence of airborne dust and smoke.

Sensor Network Based AUV Localisation

The operation of Autonomous Underwater Vehicles (AUVs) within underwater sensor network fields provides an opportunity to reuse the network infrastructure for long baseline localisation of the AUV. Computationally efficient localisation can be accomplished using off-the-shelf hardware that is comparatively inexpensive and which could already be deployed in the environment for monitoring purposes. This paper describes the development of a particle filter based localisation system which is implemented onboard an AUV in real-time using ranging information obtained from an ad-hoc underwater sensor network. An experimental demonstration of this approach was conducted in a lake with results presented illustrating network communication and localisation performance.

A low-complexity flight controller for Unmanned Aircraft Systems with constrained control allocation

In this paper, we propose a framework for joint allocation and constrained control design of flight controllers for Unmanned Aircraft Systems (UAS). The actuator configuration is used to map actuator constraint set into the space of the aircraft generalised forces. By constraining the demanded generalised forces, we ensure that the allocation problem is always feasible; and therefore, it can be solved without constraints. This leads to an allocation problem that does not require on-line numerical optimisation. Furthermore, since the controller handles the constraints, and there is no need to implement heuristics to inform the controller about actuator saturation. The latter is fundamental for avoiding Pilot Induced Oscillations (PIO) in remotely operated UAS due to the rate limit on the aircraft control surfaces.

Integrating field survey data with satellite image data to improve shallow water seagrass maps : the role of AUV and snorkeller surveys

Repeatable and accurate seagrass mapping is required for understanding seagrass ecology and supporting management decisions. For shallow (< 5 m) seagrass habitats, these maps can be created by integrating high spatial resolution imagery with field survey data. Field survey data for seagrass is often collected via snorkelling or diving. However, these methods are limited by environmental and safety considerations. Autonomous Underwater Vehicles (AUVs) are used increasingly to collect field data for habitat mapping, albeit mostly in deeper waters (>20 m). Here we demonstrate and evaluate the use and potential advantages of AUV field data collection for calibration and validation of seagrass habitat mapping of shallow waters (< 5 m), from multispectral satellite imagery. The study was conducted in the seagrass habitats of the Eastern Banks (142 km2), Moreton Bay, Australia. In the field, georeferenced photos of the seagrass were collected along transects via snorkelling or an AUV. Photos from both collection methods were analysed manually for seagrass species composition and then used as calibration and validation data to map seagrass using an established semi-automated object based mapping routine. A comparison of the relative advantages and disadvantages of AUV and snorkeller collected field data sets and their influence on the mapping routine was conducted. AUV data collection was more consistent, repeatable and safer in comparison to snorkeller transects. Inclusion of deeper water AUV data resulted in mapping of a larger extent of seagrass (~7 km2, 5 % of study area) in the deeper waters of the site. Although overall map accuracies did not differ considerably, inclusion of the AUV data from deeper water transects corrected errors in seagrass mapped at depths to 5 m, but where the bottom is visible on satellite imagery. Our results demonstrate that further development of AUV technology is justified for the monitoring of seagrass habitats in ongoing management programs.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Obstacle detection for a mining vehicle using a 2D laser

This paper discusses a number of key issues for the development of robust obstacle detection systems for autonomous mining vehicles. Strategies for obstacle detection are described and an overview of the state-of-the-art in obstacle detection for outdoor autonomous vehicles using lasers is presented, with their applicability to the mining environment noted. The development of an obstacle detection system for a mining vehicle is then detailed. This system uses a 2D laser scanner as the prime sensor and combines dead-reckoning data with laser data to create local terrain maps. The slope of the terrain maps is then used to detect potential obstacles.

A shallow water AUV for benthic and water column observations

Autonomous underwater vehicles (AUVs) are becoming commonplace in the study of inshore coastal marine habitats. Combined with shipboard systems, scientists are able to make in-situ measurements of water column and benthic properties. In CSIRO, autonomous gliders are used to collect water column data, while surface vessels are used to collect bathymetry information through the use of swath mapping, bottom grabs, and towed video systems. Although these methods have provided good data coverage for coastal and deep waters beyond 50m, there has been an increasing need for autonomous in-situ sampling in waters less than 50m deep. In addition, the collection of benthic and water column data has been conducted separately, requiring extensive post-processing to combine data streams. As such, a new AUV was developed for in-situ observations of both benthic habitat and water column properties in shallow waters. This paper provides an overview of the Starbug X AUV system, its operational characteristics including vision-based navigation and oceanographic sensor integration.

A guide to the deep-water sponges of the Aleutian Island Archipelago

The first dedicated collections of deep-water (>80 m) sponges from the central Aleutian Islands revealed a rich fauna including 28 novel species and geographical range extensions for 53 others. Based on these collections and the published literature, we now confirm the presence of 125 species (or subspecies)of deep-water sponges in the Aleutian Islands. Clearly the deep-water sponge fauna of the Aleutian Islands is extraordinarily rich and largely understudied. Submersible observations revealed that sponges, rather than deep-water corals, are the dominant feature shaping benthic habitats in the region and that they provide important refuge habitat for many species of fish and invertebrates including juvenile rockfish (Sebastes spp.) and king crabs (Lithodes sp). Examination of video footage collected along 127 km of the seafloor further indicate that there are likely hundreds of species still uncollected from the region, and many unknown to science. Furthermore, sponges are extremely fragile and easily damaged by contact with fishing gear. High rates of fishery bycatch clearly indicate a strong interaction between existing fisheries and sponge habitat. Bycatch in fisheries and fisheries-independent surveys can be a major source of information on the location of the sponge fauna, but current monitoring programs are greatly hampered by the inability of deck personnel to identify bycatch. This guide contains detailed species descriptions for 112 sponges collected in Alaska, principally in the central Aleutian Islands. It addresses bycatch identification challenges by providing fisheries observers and scientists with the information necessary to adequately identify sponge fauna. Using that identification data, areas of high abundance can be mapped and the locations of indicator species of vulnerable marine ecosystems can be determined. The guide is also designed for use by scientists making observations of the fauna in situ with submersibles, including remotely operated vehicles and autonomous underwater vehicles.

Biologically inspired visual odometer for navigation of a flying robot

Experimental research in biology has uncovered a number of different ways in which flying insects use cues derived from optical flow for navigational purposes, such as safe landing, obstacle avoidance and dead reckoning. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of bees. Specifically, we focus on the mechanisms of course stabilization behavior and visually mediated odometer by using a biological model of motion detector for the purpose of long-range goal-directed navigation in 3D environment. The performance tests of the proposed navigation method are conducted by using a blimp-type flying robot platform in uncontrolled indoor environments. The result shows that the proposed mechanism can be used for goal-directed navigation. Further analysis is also conducted in order to enhance the navigation performance of autonomous aerial vehicles. © 2003 Elsevier B.V. All rights reserved.

AUV自主导航航位推算算法的分析研究

地球形状的不规则性,各种导航传感器本身的误差,以及仪器的安装偏差等,使得AUV(自治水下机器人)在进行远距离自主航行时,自主导航的精度大大下降。针对以上问题及实际工程需要,论文对AUV自主导航的航位推算算法做了进一步研究并加以改进,以提高其自主导航精度。最后,利用2004年中国科学院沈阳自动化所水下机器人研究中心进行AUV湖试所获得的数据,对文中提出的算法进行了验证。结果表明,AUV的自主导航精度得到大大提高,可以用于修正原来的自主导航算法。

AUV自主导航航位推算算法的研究

对AUV(AutonomousUnderwaterVehicle)自主导航的航位推算算法做了进一步研究并加以改进,以提高其自主导航精度.然后,利用AUV湖试所获得的数据,对本文提出的修正算法进行了验证.结果表明, AUV的自主导航精度得到很大提高,可以用于修正原来的自主导航算法.

水下机器人神经网络自适应逆控制

水下环境的复杂性以及自身模型的不确定性,给水下机器人的控制带来很大困难。针对水下机器人的特点和控制方面所存在的问题,提出了基于预测 校正控制策略的水下机器人神经网络自适应逆控制结构及训练算法。通过在线辨识系统的前向模型,估计出系统的Jacobian矩阵,然后采用预报误差法实现控制器的自适应。同时,为了提高系统对于外扰的鲁棒性,在伪线性回归算法的基础上,在评价函数中引入微分项。理论分析和仿真结果表明,与原来的算法相比,微分项的引入改善了系统对于外扰的鲁棒性和动态性能。

自治水下机器人的一种鲁棒自适应控制方法研究

本文针对自治水下机器人AUVs(Autonomous Underwater Vehicles)水下工作环境、动力学建模和运动控制的特点。采用了一种基于Backstepping的鲁棒自适应控制方法，实现AUV水平侧移和航向控制，并利用AUV-CR02模型进行了仿真实验。仿真实验结果表明该控制方法的控制效果明显优于PID控制。特别是在有模型不确定和不确定环绕干扰时，表现出良好的鲁律性和自适应性。

从有缆遥控水下机器人到自治水下机器人

文章给出了水下机器人的定义 ,依据定义进行了分类 ,简要回顾了几类重要水下机器人的进展 ,指出了无人无缆自治水下机器人 (AUVs)是当今水下机器人研究与开发的热点 ,介绍了最近 2 0年沈阳自动化研究所与国内外有关单位合作 ,在水下机器人领域从无人有缆遥控水下机器人 (ROVs)到AUVs的研究开发工作 ,它从一个侧面反映了我国在这一领域的进展情况。

自治式水下机器人回收系统的研究与设计

本文介绍了我们承担研制的国家“８６３”计划１０００ｍ及６０００ｍ无缆水下机器人的回收系统．回收系统在４级海况下不用专用母船能够成功地回收水下机器人，依据母船、海况、水下机器人及其他具体情况，介绍了两种不同的回收方案和回收器，经海上试验证明是有效和可行的。