Vision based control for fixed wing UAVs inspecting locally linear infrastructure using skid-to-turn maneuvers

The following paper proposes a novel application of Skid-to-Turn maneuvers for fixed wing Unmanned Aerial Vehicles (UAVs) inspecting locally linear infrastructure. Fixed wing UAVs, following the design of manned aircraft, traditionally employ Bank-to-Turn maneuvers to change heading and thus direction of travel. Commonly overlooked is the effect these maneuvers have on downward facing body fixed sensors, which as a result of bank, point away from the feature during turns. By adopting Skid-to-Turn maneuvers, the aircraft is able change heading whilst maintaining wings level flight, thus allowing body fixed sensors to maintain a downward facing orientation. Eliminating roll also helps to improve data quality, as sensors are no longer subjected to the swinging motion induced as they pivot about an axis perpendicular to their line of sight. Traditional tracking controllers that apply an indirect approach of capturing ground based data by flying directly overhead can also see the feature off center due to steady state pitch and roll required to stay on course. An Image Based Visual Servo controller is developed to address this issue, allowing features to be directly tracked within the image plane. Performance of the proposed controller is tested against that of a Bank-to-Turn tracking controller driven by GPS derived cross track error in a simulation environment developed to simulate the field of view of a body fixed camera.

Estimation and control for an open-source quadcopter

This paper describes modelling, estimation and control of the horizontal translational motion of an open-source and cost effective quadcopter — the MikroKopter. We determine the dynamics of its roll and pitch attitude controller, system latencies, and the units associated with the values exchanged with the vehicle over its serial port. Using this we create a horizontal-plane velocity estimator that uses data from the built-in inertial sensors and an onboard laser scanner, and implement translational control using a nested control loop architecture. We present experimental results for the model and estimator, as well as closed-loop positioning.

Choosing landmarks for risky planning

This work examines the effect of landmark placement on the efficiency and accuracy of risk-bounded searches over probabilistic costmaps for mobile robot path planning. In previous work, risk-bounded searches were shown to offer in excess of 70% efficiency increases over normal heuristic search methods. The technique relies on precomputing distance estimates to landmarks which are then used to produce probability distributions over exact heuristics for use in heuristic searches such as A* and D*. The location and number of these landmarks therefore influence greatly the efficiency of the search and the quality of the risk bounds. Here four new methods of selecting landmarks for risk based search are evaluated. Results are shown which demonstrate that landmark selection needs to take into account the centrality of the landmark, and that diminishing rewards are obtained from using large numbers of landmarks.

Path planning using surface shape and ground properties

This paper presents a path planning technique for ground vehicles that accounts for the dynamics of the vehicle, the topography of the terrain and the wheel/ground interaction properties such as friction. The first two properties can be estimated using well known sensors and techniques, but the third is not often estimated even though it has a significant effect on the motion of a high-speed vehicle. We introduce a technique which allows the estimation of wheel slip from which frictional parameters can be inferred. We present simulation results which show the importance of modelling topography and ground properties and experimental results which show how ground properties can be estimated along a 350m outdoor traverse.

Learning multidimensional joint control of a robot using receding horizon locally weighted regression

In this paper we explore the ability of a recent model-based learning technique Receding Horizon Locally Weighted Regression (RH-LWR) useful for learning temporally dependent systems. In particular this paper investigates the application of RH-LWR to learn control of Multiple-input Multiple-output robot systems. RH-LWR is demonstrated through learning joint velocity and position control of a three Degree of Freedom (DoF) rigid body robot.

The safety risk management of unmanned aircraft systems

The safety risk management process describes the systematic application of management policies, procedures and practices to the activities of communicating, consulting, establishing the context, and identifying, analysing, evaluating, treating, monitoring and reviewing risk. This process is undertaken to provide assurances that the risks of a particular unmanned aircraft system activity have been managed to an acceptable level. The safety risk management process and its outcomes form part of the documented safety case necessary to obtain approvals for unmanned aircraft system operations. It also guides the development of an organisation’s operations manual and is a primary component of an organisation’s safety management system. The aim of this chapter is to provide existing risk practitioners with a high level introduction to some of the unique issues and challenges in the application of the safety risk management process to unmanned aircraft systems. The scope is limited to safety risks associated with the operation of unmanned aircraft in the civil airspace system and over inhabited areas. The structure of the chapter is based on the safety risk management process as defined by the international risk management standard ISO 31000:2009 and draws on aviation safety resources provided by International Civil Aviation Organization, the Federal Aviation Administration and U.S. Department of Defense. References to relevant aviation safety regulations, programs of research and fielded systems are also provided.

Towards condition-invariant sequence-based route recognition

In this paper we present a novel algorithm for localization during navigation that performs matching over local image sequences. Instead of calculating the single location most likely to correspond to a current visual scene, the approach finds candidate matching locations within every section (subroute) of all learned routes. Through this approach, we reduce the demands upon the image processing front-end, requiring it to only be able to correctly pick the best matching image from within a short local image sequence, rather than globally. We applied this algorithm to a challenging downhill mountainbiking visual dataset where there was significant perceptual or environment change between repeated traverses of the environment, and compared performance to applying the feature-based algorithm FAB-MAP. The results demonstrate the potential for localization using visual sequences, even when there are no visual features that can be reliably detected.

Is the public sector ready to collaborate? Human resource implications of collaborative Arrangements

Relational governance arrangements across agencies and sectors have become prevalent as a means for government to become more responsive and effective in addressing complex, large scale or ‘wicked’ problems. The primary characteristic of such ‘collaborative’ arrangements is the utilisation of the joint capacities of multiple organisations to achieve collaborative advantage, which Huxham (1993) defines as the attainment of creative outcomes that are beyond the ability of single agencies to achieve. Attaining collaborative advantage requires organisations to develop collaborative capabilities that prepare organisations for collaborative practice (Huxham, 1993b). Further, collaborations require considerable investment of staff effort that could potentially be used beneficially elsewhere by both the government and non-government organisations involved in collaboration (Keast and Mandell, 2010). Collaborative arrangements to deliver services therefore requires a reconsideration of the way in which resources, including human resources, are conceptualised and deployed as well as changes to both the structure of public service agencies and the systems and processes by which they operate (Keast, forthcoming). A main aim of academic research and theorising has been to explore and define the requisite characteristics to achieve collaborative advantage. Such research has tended to focus on definitional, structural (Turrini, Cristofoli, Frosini, & Nasi, 2009) and organisational (Huxham, 1993) aspects and less on the roles government plays within cross-organisational or cross-sectoral arrangements. Ferlie and Steane (2002) note that there has been a general trend towards management led reforms of public agencies including the HRM practices utilised. Such trends have been significantly influenced by New Public Management (NPM) ideology with limited consideration to the implications for HRM practice in collaborative, rather than market contexts. Utilising case study data of a suite of collaborative efforts in Queensland, Australia, collected over a decade, this paper presents an examination of the network roles government agencies undertake. Implications for HRM in public sector agencies working within networked arrangements are drawn and implications for job design, recruitment, deployment and staff development are presented. The paper also makes theoretical advances in our understanding of Strategic Human Resource Management (SHRM) in network settings. While networks form part of the strategic armoury of government, networks operate to achieve collaborative advantage. SHRM with its focus on competitive advantage is argued to be appropriate in market situations, however is not an ideal conceptualisation in network situations. Commencing with an overview of literature on networks and network effectiveness, the paper presents the case studies and methodology; provides findings from the case studies in regard to the roles of government to achieve collaborative advantage and implications for HRM practice are presented. Implications for SHRM are considered.

Multi-sensor data fusion for UAV navigation during landing operations

This paper presents a practical framework to synthesize multi-sensor navigation information for localization of a rotary-wing unmanned aerial vehicle (RUAV) and estimation of unknown ship positions when the RUAV approaches the landing deck. The estimation performance of the visual tracking sensor can also be improved through integrated navigation. Three different sensors (inertial navigation, Global Positioning System, and visual tracking sensor) are utilized complementarily to perform the navigation tasks for the purpose of an automatic landing. An extended Kalman filter (EKF) is developed to fuse data from various navigation sensors to provide the reliable navigation information. The performance of the fusion algorithm has been evaluated using real ship motion data. Simulation results suggest that the proposed method can be used to construct a practical navigation system for a UAV-ship landing system.

Urban food futures : ICTs and opportunities

Food is a vital foundation of all human life. It is essential to a myriad of political, socio-cultural, economic and environmental practices throughout history. However, those practices of food production, consumption, and distribution have the potential to now go through immensely transformative shifts as network technologies become increasingly embedded in every domain of contemporary life. Information and communication technologies (ICTs) are one of the key foundations of global functionality and sustenance today and undoubtedly will continue to present new challenges and opportunities for the future. As such, this Symposium will bring together leading scholars across disciplines to address challenges and opportunities at the intersection of food and ICTs in everyday urban environment. In particular, the discussion will revolve around the question: What are the key roles that network technologies play in re-shaping the food systems at micro- to macroscopic level? The symposium will contribute a unique perspective on urban food futures through the lens of network society paradigm where ICTs enable innovations in production, organisation, and communication within society. Some of the topics addressed will include encouraging transparency in food commodity chains; value of cultural understanding and communication in global food sustainability; and technologies to social inclusion; all of which evoke and examine the question surrounding networked individuals as changes catalysts for urban food futures. The event will provide an avenue for new discussions and speculations on key issues surrounding urban food futures in the network era, with a particular focus on bottom-up micro actions that challenge the existing food systems towards a broader sociocultural, political, technological, and environmental transformations. One central area of concern is that current systems of food production, distribution, and consumption do not ensure food security for the future, but rather seriously threaten it. With the recent unprecedented scale of urban growth and rise of middle-class, the problem continues to intensify. This situation requires extensive distribution networks to feed urban residents, and therefore poses significant infrastructural challenges to both the public and private sectors. The symposium will also address the transferability of citizen empowerment that network technologies enable as demonstrated in various significant global political transformations from the bottom-up, such as the recent Egyptian Youth Revolution. Another key theme of the discussion will be the role of ICTs (and the practices that they mediate) in fostering transparency in commodity chains. The symposium will ask what differences these technologies can make on the practices of food consumption and production. After discussions, we will initiate an international network of food-thinkers and actors that will function as a platform for knowledge sharing and collaborations. The participants will be invited to engage in planning for the on-going future development of the network.

Towards automatic object segmentation with sequential multiple views

Object segmentation is one of the fundamental steps for a number of robotic applications such as manipulation, object detection, and obstacle avoidance. This paper proposes a visual method for incorporating colour and depth information from sequential multiview stereo images to segment objects of interest from complex and cluttered environments. Rather than segmenting objects using information from a single frame in the sequence, we incorporate information from neighbouring views to increase the reliability of the information and improve the overall segmentation result. Specifically, dense depth information of a scene is computed using multiple view stereo. Depths from neighbouring views are reprojected into the reference frame to be segmented compensating for imperfect depth computations for individual frames. The multiple depth layers are then combined with color information from the reference frame to create a Markov random field to model the segmentation problem. Finally, graphcut optimisation is employed to infer pixels belonging to the object to be segmented. The segmentation accuracy is evaluated over images from an outdoor video sequence demonstrating the viability for automatic object segmentation for mobile robots using monocular cameras as a primary sensor.

Growing local participation through long term design

Our research explores the design of networked technologies to facilitate local suburban communications and to encourage people to engage with their local community. While there are many investigations of interaction designs for networked technologies, most research utilises small exercises, workshops or other short-term studies to investigate interaction designs. However, we have found these short-term methods to be ineffective in the context of understanding local community interaction. Moreover we find that people are resistant to putting their time into workshops and exercises, understandably so because these are academic practices, not local community practices. Our contribution is to detail a long term embedded design approach in which we interact with the community over the long term in the course of normal community goings-on with an evolving exploratory prototype. This paper discusses the embedded approach to working in the wild for extended field research.

Feature-based visual odometry and featureless place recognition for SLAM in 2.5D environments

In this paper we present a novel algorithm for localization during navigation that performs matching over local image sequences. Instead of calculating the single location most likely to correspond to a current visual scene, the approach finds candidate matching locations within every section (subroute) of all learned routes. Through this approach, we reduce the demands upon the image processing front-end, requiring it to only be able to correctly pick the best matching image from within a short local image sequence, rather than globally. We applied this algorithm to a challenging downhill mountain biking visual dataset where there was significant perceptual or environment change between repeated traverses of the environment, and compared performance to applying the feature-based algorithm FAB-MAP. The results demonstrate the potential for localization using visual sequences, even when there are no visual features that can be reliably detected.

Towards lifelong navigation and mapping in an office environment

This paper addresses the challenge of developing robots that map and navigate autonomously in real world, dynamic environments throughout the robot’s entire lifetime – the problem of lifelong navigation. Static mapping algorithms can produce highly accurate maps, but have found few applications in real environments that are in constant flux. Environments change in many ways: both rapidly and gradually, transiently and permanently, geometrically and in appearance. This paper demonstrates a biologically inspired navigation algorithm, RatSLAM, that uses principles found in rodent neural circuits. The algorithm is demonstrated in an office delivery challenge where the robot was required to perform mock deliveries to goal locations in two different buildings. The robot successfully completed 1177 out of 1178 navigation trials over 37 hours of around the clock operation spread over 11 days.