Height estimation for an autonomous helicopter

Height is a critical variable for helicopter hover control. In this paper we discuss, and present experimental results for, two different height sensing techniques: ultrasonic and stereo imaging, which have complementary characteristics. Feature-based stereo is used which provides a basis for visual odometry and attitude estimation in the future.

Automation of underground LHD and truck haulage

This paper reviews the state-of-the-art in the automation of underground mining vehicles and reports on the development of an autonomous navigation system under development through the CMTE with sponsorship arranged by AMIRA. Past attempts at automating LHDs and haul trucks are described and their particular strengths and weaknesses are discussed. The auto-guidance system being developed overcomes some of the limitations of state-of-the-art prototype æcommercialÆ systems. It can be retrofitted to existing remote controlled vehicles, uses minimum installed infrastructure and is flexible enough for rapid relocation to alternate routes. The navigation techniques use data fusion of two separate sets of sensors combining natural feature recognition, nodal maps and inertial navigation techniques. Collision detection is incorporated and people and other traffic are excluded from the tramming area. This paper describes the work being done by the group with regard to auto-tramming and also outlines the future goals.

An autonomous mobile manipulator for collecting sample containers

In this work we present an autonomous mobile ma- nipulator that is used to collect sample containers in an unknown environment. The manipulator is part of a team of heterogeneous mobile robots that are to search and identify sample containers in an unknown environment. A map of the environment along with possible positions of sample containers are shared between the robots in the team by using a cloud-based communication interface. To grasp a container with its manipulator arm the robot has to place itself in a position suitable for the manipulation task. This optimal base placement pose is selected by querying a precomputed inverse reachability database.

Design and evaluation of decision and control strategies for autonomous vision-based see and avoid systems

This paper details the design and performance assessment of a unique collision avoidance decision and control strategy for autonomous vision-based See and Avoid systems. The general approach revolves around re-positioning a collision object in the image using image-based visual servoing, without estimating range or time to collision. The decision strategy thus involves determining where to move the collision object, to induce a safe avoidance manuever, and when to cease the avoidance behaviour. These tasks are accomplished by exploiting human navigation models, spiral motion properties, expected image feature uncertainty and the rules of the air. The result is a simple threshold based system that can be tuned and statistically evaluated by extending performance assessment techniques derived for alerting systems. Our results demonstrate how autonomous vision-only See and Avoid systems may be designed under realistic problem constraints, and then evaluated in a manner consistent to aviation expectations.

Cooperative diversity for performance enhancement of vehicular ad hoc networks

This project was a step forward in introducing suitable cooperative diversity transmission techniques for vehicle to vehicle communications. The contributions are intended to aid in the successful implementation of future vehicular safety and autonomous controlling systems. Several protocols were introduced for vehicles to communicate effectively without losing connectivity. This study investigated novel protocols in terms of diversity-multiplexing trade-off and outage for a range of potential vehicular safety and infotainment applications.

From vision to actions: Towards adaptive and autonomous humanoid robots

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

A Survey of autonomous vision-based See and Avoid for Unmanned Aircraft Systems

This paper provides a comprehensive review of the vision-based See and Avoid problem for unmanned aircraft. The unique problem environment and associated constraints are detailed, followed by an in-depth analysis of visual sensing limitations. In light of such detection and estimation constraints, relevant human, aircraft and robot collision avoidance concepts are then compared from a decision and control perspective. Remarks on system evaluation and certification are also included to provide a holistic review approach. The intention of this work is to clarify common misconceptions, realistically bound feasible design expectations and offer new research directions. It is hoped that this paper will help us to unify design efforts across the aerospace and robotics communities.

‘Lemon Laws’: an inquiry into consumer protections and remedies for buyers of new motor vehicles

1. Under the Terms of Reference for the Committee’s Inquiry, ‘lemons’ are defined as ‘new motor vehicles with numerous, severe defects that re-occur despite multiple repair attempts or where defects have caused a new motor vehicle to be out of service for a prolonged period of time’. Consumers are currently protected in relation to lemon purchases by the Australian Consumer Law (ACL) located in Schedule 2 of the Competition and Consumer Act 2010 (Cth) (CCA). The ACL applies as a law of Queensland pursuant to the Fair Trading Act 1989 (Qld). The voluntary recall and consumer guarantees law took effect on 1 January 2011. 2. In 2006, the Government of Victoria made a commitment to introduce a lemon law into the provisions of the then Fair Trading Act 1999 (Vic). The public consultation process on the proposal to introduce a lemon law for motor vehicle purchases in Victoria was conducted by Ms Janice Munt MP, with the assistance of Consumer Affairs Victoria (CAV). CAV released an Issues Paper to canvas with industry and the community options for the development and introduction of a motor vehicle lemon law.(Consumer Affairs Victoria, Introducing Victorian motor vehicle lemon laws, Issues Paper, (September, 2007). 3. A CAV report prepared by Janice Munt MP was released in July, 2008 (Consumer Affairs Victoria, Motor Cars: A report on the motor vehicle lemon law consultations (July 2008) (Victorian Lemon Law Report). However, the Victorian proposal was overtaken by events leading to the adoption of a uniform consumer protection law in all Australian jurisdictions, the ACL. 4. The structure of this submission is to consider first the three different bases upon which consumers can obtain relief for economic loss arising from defects in motor vehicles. The second part of the submission considers the difficulties encountered by consumers in litigating motor vehicle disputes in the courts and tribunals. The third part of the submission examines the approach taken in other jurisdictions to resolving motor vehicle disputes. The final part of the submission considers a number of possible reforms that could be made to the existing law and its enforcement to reduce consumer detriment arising from the purchase of ‘lemon’ motor vehicles. 5. There are three principal bases upon which a consumer can obtain redress for defects in new motor vehicles under the ACL. The first is where the manufacturer admits liability and initiates the voluntary recall procedure provided for in s 128 of the ACL. Under this basis the manufacturer generally repairs or replaces the part subject to the recall free of charge. The second basis is where the manufacturer or dealer denies liability and the consumer is initiates proceedings in the court or tribunal seeking a statutory remedy under the ACL, the nature of which will depend on whether the failure to comply with the consumer guarantee was major or not. The third basis upon which a consumer can obtain redress is pursuant to public enforcement by the ACCC. Each basis will be considered in this part. What all three bases have in common is the need to conduct an investigation to identify the nature of the defect and how it arose.

Design and flight testing of a bio-inspired plume tracking algorithm for unmanned serial vehicles

There is an increased interest on the use of UAVs for environmental research such as tracking bush fires, volcanic eruptions, chemical accidents or pollution sources. The aim of this paper is to describe the theory and results of a bio-inspired plume tracking algorithm. A method for generating sparse plumes in a virtual environment was also developed. Results indicated the ability of the algorithms to track plumes in 2D and 3D. The system has been tested with hardware in the loop (HIL) simulations and in flight using a CO2 gas sensor mounted to a multi-rotor UAV. The UAV is controlled by the plume tracking algorithm running on the ground control station (GCS).

MPC controlled multirotor with suspended slung load: System architecture and visual load detection

There is an increased interest in the use of Unmanned Aerial Vehicles for load transportation from environmental remote sensing to construction and parcel delivery. One of the main challenges is accurate control of the load position and trajectory. This paper presents an assessment of real flight trials for the control of an autonomous multi-rotor with a suspended slung load using only visual feedback to determine the load position. This method uses an onboard camera to take advantage of a common visual marker detection algorithm to robustly detect the load location. The load position is calculated using an onboard processor, and transmitted over a wireless network to a ground station integrating MATLAB/SIMULINK and Robotic Operating System (ROS) and a Model Predictive Controller (MPC) to control both the load and the UAV. To evaluate the system performance, the position of the load determined by the visual detection system in real flight is compared with data received by a motion tracking system. The multi-rotor position tracking performance is also analyzed by conducting flight trials using perfect load position data and data obtained only from the visual system. Results show very accurate estimation of the load position (~5% Offset) using only the visual system and demonstrate that the need for an external motion tracking system is not needed for this task.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Development of a robust framework for an outdoor mobile manipulation UAV

There is a growing interest to autonomously collect or manipulate objects in remote or unknown environments, such as mountains, gullies, bush-land, or rough terrain. There are several limitations of conventional methods using manned or remotely controlled aircraft. The capability of small Unmanned Aerial Vehicles (UAV) used in parallel with robotic manipulators could overcome some of these limitations. By enabling the autonomous exploration of both naturally hazardous environments, or areas which are biologically, chemically, or radioactively contaminated, it is possible to collect samples and data from such environments without directly exposing personnel to such risks. This paper covers the design, integration, and initial testing of a framework for outdoor mobile manipulation UAV. The framework is designed to allow further integration and testing of complex control theories, with the capability to operate outdoors in unknown environments. The results obtained act as a reference for the effectiveness of the integrated sensors and low-level control methods used for the preliminary testing, as well as identifying the key technologies needed for the development of an outdoor capable system.

A hybrid agent architecture for modeling autonomous agents in SAGE

This paper highlights the Hybrid agent construction model being developed that allows the description and development of autonomous agents in SAGE (Scalable, fault Tolerant Agent Grooming Environment) - a second generation FIPA-Compliant Multi-Agent system. We aim to provide the programmer with a generic and well defined agent architecture enabling the development of sophisticated agents on SAGE, possessing the desired properties of autonomous agents - reactivity, pro-activity, social ability and knowledge based reasoning. © Springer-Verlag Berlin Heidelberg 2005.

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.

Revocation and update of trust in autonomous delay tolerant networks

A Delay Tolerant Network (DTN) is a dynamic, fragmented, and ephemeral network formed by a large number of highly mobile nodes. DTNs are ephemeral networks with highly mobile autonomous nodes. This requires distributed and self-organised approaches to trust management. Revocation and replacement of security credentials under adversarial influence by preserving the trust on the entity is still an open problem. Existing methods are mostly limited to detection and removal of malicious nodes. This paper makes use of the mobility property to provide a distributed, self-organising, and scalable revocation and replacement scheme. The proposed scheme effectively utilises the Leverage of Common Friends (LCF) trust system concepts to revoke compromised security credentials, replace them with new ones, whilst preserving the trust on them. The level of achieved entity confidence is thereby preserved. Security and performance of the proposed scheme is evaluated using an experimental data set in comparison with other schemes based around the LCF concept. Our extensive experimental results show that the proposed scheme distributes replacement credentials up to 35% faster and spreads spoofed credentials of strong collaborating adversaries up to 50% slower without causing any significant increase on the communication and storage overheads, when compared to other LCF based schemes.