Field and Service Robotics: Results of the 9th International Conference

The Field and Service Robotics (FSR) conference is a single track conference with a specific focus on field and service applications of robotics technology. The goal of FSR is to report and encourage the development of field and service robotics. These are non-factory robots, typically mobile, that must operate in complex and dynamic environments. Typical field robotics applications include mining, agriculture, building and construction, forestry, cargo handling and so on. Field robots may operate on the ground (of Earth or planets), under the ground, underwater, in the air or in space. Service robots are those that work closely with humans, importantly the elderly and sick, to help them with their lives. The first FSR conference was held in Canberra, Australia, in 1997. Since then the meeting has been held every 2 years in Asia, America, Europe and Australia. It has been held in Canberra, Australia (1997), Pittsburgh, USA (1999), Helsinki, Finland (2001), Mount Fuji, Japan (2003), Port Douglas, Australia (2005), Chamonix, France (2007), Cambridge, USA (2009), Sendai, Japan (2012) and most recently in Brisbane, Australia (2013). This year we had 54 submissions of which 36 were selected for oral presentation. The organisers would like to thank the international committee for their invaluable contribution in the review process ensuring the overall quality of contributions. The organising committee would also like to thank Ben Upcroft, Felipe Gonzalez and Aaron McFadyen for helping with the organisation and proceedings. and proceedings. The conference was sponsored by the Australian Robotics and Automation Association (ARAA), CSIRO, Queensland University of Technology (QUT), Defence Science and Technology Organisation Australia (DSTO) and the Rio Tinto Centre for Mine Automation, University of Sydney.

Evaluating unmanned aircraft systems for deployment in plant biosecurity

This report provides a qualitative evaluation of Unmanned Aircraft Systems (UAS) and on-board sensor technology for use in plant biosecurity in the Australian context. The more general term UAS describes both the Unmanned Aerial Vehicle (UAV) and all supporting components required to operate it. This may include a ground station, operator or pilot, and a launch and recovery device for example. The focus is to identify how and under what circumstances UAS may be useful for plant biosecurity. This can be used to help guide future decisions regarding investment in UAS for plant biosecurity.

Engaging students (and their teachers) in STEM through robotics

Robotics@QUT is a university outreach program aimed at building pre- and in-service teacher capacity to encourage interest in Science, Technology, Engineering and Mathematics (STEM) subjects with school children from low socio-economic status areas. Currently over 35 schools are involved in the outreach program. Professional Development workshops are provided to teachers to build their knowledge in implementing robotics-based STEM activities in their classrooms, robotics loan kits are provided, and pre-service teacher visits arranged to provide the teachers with on-going support. The program also provides opportunities for school students to engage in robotics-based on-campus activities and competitions and is seen as a way to build aspirations for university. This paper presents an interim evaluation that examines the value of the Robotics@QUT program for the teachers, pre-service teachers and school students participating in the program. Surveys were administered to determine the participants’ perceived benefits of being involved and their perceptions of the program. The data gathered from the teachers showed that they had gained knowledge and confidence and felt that the Robotics@QUT program had assisted them to deliver engaging robotics-based STEM activities in their classrooms. The pre-service teachers’ responses focused on benefits for themselves, for their future teaching careers and for the school students involved. The school students’ responses focused on their increased knowledge and confidence to pursue future STEM studies and careers.

Multi-rotor with suspended load: System dynamics and control toolbox

There is an increasing demand for Unmanned Aerial Systems (UAS) to carry suspended loads as this can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. The constant variation in operating point induced by the slung load also causes conventional controllers to demand increased control effort. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present a novel controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions. The paper describes a System Dynamics and Control Simulation Toolbox for use with MATLAB/SIMULINK which includes a detailed simulation of the multi-rotor and slung load as well as a predictive controller to manage the nonlinear dynamics whilst accounting for system constraints. It is demonstrated that the controller simultaneously tracks specified waypoints and actively damps large slung load oscillations. A linear-quadratic regulator (LQR) is derived and control performance is compared. Results show the improved performance of the predictive controller for a larger flight envelope, including aggressive manoeuvres and large slung load displacements. The computational cost remains relatively small, amenable to practical implementations.

Sensors and control for mining robotics

This paper discusses some of the sensing technologies and control approaches available for guiding robot manipulators for a class of underground mining tasks including drilling jumbos, bolting arms, shotcreters or explosive chargers. Data acquired with such sensors, in the laboratory and underground, is presented.

A dynamic navigation model for unmanned aircraft systems and an application to autonomous front-on environmental sensing and photography using low-cost sensor systems

This paper presents an unmanned aircraft system (UAS) that uses a probabilistic model for autonomous front-on environmental sensing or photography of a target. The system is based on low-cost and readily-available sensor systems in dynamic environments and with the general intent of improving the capabilities of dynamic waypoint-based navigation systems for a low-cost UAS. The behavioural dynamics of target movement for the design of a Kalman filter and Markov model-based prediction algorithm are included. Geometrical concepts and the Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of a target, thus delivering a new waypoint for autonomous navigation. The results of the application to aerial filming with low-cost UAS are presented, achieving the desired goal of maintained front-on perspective without significant constraint to the route or pace of target movement.

Remote Sensing and Data Collection from Solar Powered Wireless Sensor Network using an Unmanned Aerial Vehicle System

Sensor networks for environmental monitoring present enormous benefits to the community and society as a whole. Currently there is a need for low cost, compact, solar powered sensors suitable for deployment in rural areas. The purpose of this research is to develop both a ground based wireless sensor network and data collection using unmanned aerial vehicles. The ground based sensor system is capable of measuring environmental data such as temperature or air quality using cost effective low power sensors. The sensor will be configured such that its data is stored on an ATMega16 microcontroller which will have the capability of communicating with a UAV flying overhead using UAV communication protocols. The data is then either sent to the ground in real time or stored on the UAV using a microcontroller until it lands or is close enough to enable the transmission of data to the ground station.

LiDAR Augmented Optimal Path Planning for Unmanned Aerial Vehicle during Remote Sensing at Low Altitude and Network Sensor Data Acquisition

This technical report describes a Light Detection and Ranging (LiDAR) augmented optimal path planning at low level flight methodology for remote sensing and sampling Unmanned Aerial Vehicles (UAV). The UAV is used to perform remote air sampling and data acquisition from a network of sensors on the ground. The data that contains information on the terrain is in the form of a 3D point clouds maps is processed by the algorithms to find an optimal path. The results show that the method and algorithm are able to use the LiDAR data to avoid obstacles when planning a path from a start to a target point. The report compares the performance of the method as the resolution of the LIDAR map is increased and when a Digital Elevation Model (DEM) is included. From a practical point of view, the optimal path plan is loaded and works seemingly with the UAV ground station and also shows the UAV ground station software augmented with more accurate LIDAR data.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Evaluation of aerial photography for predicting trends in structural attributes of Australian woodland including comparison with ground-based monitoring data

The accurate assessment of trends in the woody structure of savannas has important implications for greenhouse accounting and land-use industries such as pastoralism. Two recent assessments of live woody biomass change from north-east Australian eucalypt woodland between the 1980s and 1990s present divergent results. The first estimate is derived from a network of permanent monitoring plots and the second from woody cover assessments from aerial photography. The differences between the studies are reviewed and include sample density, spatial scale and design. Further analyses targeting potential biases in the indirect aerial photography technique are conducted including a comparison of basal area estimates derived from 28 permanent monitoring sites with basal area estimates derived by the aerial photography technique. It is concluded that the effect of photo-scale; or the failure to include appropriate back-transformation of biomass estimates in the aerial photography study are not likely to have contributed significantly to the discrepancy. However, temporal changes in the structure of woodlands, for example, woodlands maturing from many smaller trees to fewer larger trees or seasonal changes, which affect the relationship between cover and basal area could impact on the detection of trends using the aerial photography technique. It is also possible that issues concerning photo-quality may bias assessments through time, and that the limited sample of the permanent monitoring network may inadequately represent change at regional scales

Frequency and precision of aerial surveys for kangaroo management

The appropriate frequency and precision for surveys of wildlife populations represent a trade-off between survey cost and the risk of making suboptimal management decisions because of poor survey data. The commercial harvest of kangaroos is primarily regulated through annual quotas set as proportions of absolute estimates of population size. Stochastic models were used to explore the effects of varying precision, survey frequency and harvest rate on the risk of quasiextinction for an arid-zone and a more mesic-zone kangaroo population. Quasiextinction probability increases in a sigmoidal fashion as survey frequency is reduced. The risk is greater in more arid regions and is highly sensitive to harvest rate. An appropriate management regime involves regular surveys in the major harvest areas where harvest rate can be set close to the maximum sustained yield. Outside these areas, survey frequency can be reduced in relatively mesic areas and reduced in arid regions when combined with lowered harvest rates. Relative to other factors, quasiextinction risk is only affected by survey precision (standard error/mean × 100) when it is >50%, partly reflecting the safety of the strategy of harvesting a proportion of a population estimate.

The influence of animal mobility on the assumption of uniform distances in aerial line-transect surveys

Line-transect distance sampling is a widely used method for estimating animal density from aerial surveys. Analysis of line-transect distance data usually relies on a requirement that the statistical distribution of distances of animal groups from the transect line is uniform. We show that this requirement is satisfied by the survey design if all other assumptions of distance sampling hold, but it can be violated by consistent survey problems such as responsive movement of the animals towards or away from the observer. We hypothesise that problems with the uniform requirement are unlikely to be encountered for immobile taxa, but might become substantial for species of high mobility. We test evidence for non-uniformity using double-observer distance data from two aerial surveys of five species with a spectrum of mobility capabilities and tendencies. No clear evidence against uniformity was found for crabeater seals or emperor penguins on the pack-ice in East Antarctica, while minor non-uniformity consistent with responsive movement up to 30 m was found for Adelie penguins. Strong evidence of either non-uniformity or a failure of the capture-recapture validating method was found for eastern grey kangaroos and red kangaroos in Queensland.

A comparison of mark-recapture distance-sampling methods applied to aerial surveys of eastern grey kangaroos

Aerial surveys of kangaroos (Macropus spp.) in Queensland are used to make economically important judgements on the levels of viable commercial harvest. Previous analysis methods for aerial kangaroo surveys have used both mark-recapture methodologies and conventional distance-sampling analyses. Conventional distance sampling has the disadvantage that detection is assumed to be perfect on the transect line, while mark-recapture methods are notoriously sensitive to problems with unmodelled heterogeneity in capture probabilities. We introduce three methodologies for combining together mark-recapture and distance-sampling data, aimed at exploiting the strengths of both methodologies and overcoming the weaknesses. Of these methods, two are based on the assumption of full independence between observers in the mark-recapture component, and this appears to introduce more bias in density estimation than it resolves through allowing uncertain trackline detection. Both of these methods give lower density estimates than conventional distance sampling, indicating a clear failure of the independence assumption. The third method, termed point independence, appears to perform very well, giving credible density estimates and good properties in terms of goodness-of-fit and percentage coefficient of variation. Estimated densities of eastern grey kangaroos range from 21 to 36 individuals km-2, with estimated coefficients of variation between 11% and 14% and estimated trackline detection probabilities primarily between 0.7 and 0.9.

Development and Simulation of a Variable Rate Controller for an Air search and Air Sampling Unmanned Aerial System

This report describes the development and simulation of a variable rate controller for a 6-degree of freedom nonlinear model. The variable rate simulation model represents an off the shelf autopilot. Flight experiment involves risks and can be expensive. Therefore a dynamic model to understand the performance characteristics of the UAS in mission simulation before actual flight test or to obtain parameters needed for the flight is important. The control and guidance is implemented in Simulink. The report tests the use of the model for air search and air sampling path planning. A GUI in which a set of mission scenarios, in which two experts (mission expert, i.e. air sampling or air search and an UAV expert) interact, is presented showing the benefits of the method.