Redundant sensing for localisation in outdoor industrial environments

We describe our experiences with automating a large fork-lift type vehicle that operates outdoors and in all weather. In particular, we focus on the use of independent and robust localisation systems for reliable navigation around the worksite. Two localisation systems are briefly described. The first is based on laser range finders and retro-reflective beacons, and the second uses a two camera vision system to estimate the vehicle’s pose relative to a known model of the surrounding buildings. We show the results from an experiment where the 20 tonne experimental vehicle, an autonomous Hot Metal Carrier, was conducting autonomous operations and one of the localisation systems was deliberately made to fail.

Coverage algorithms for an under-actuated car-like vehicle in an uncertain environment

A coverage algorithm is an algorithm that deploys a strategy as to how to cover all points in terms of a given area using some set of sensors. In the past decades a lot of research has gone into development of coverage algorithms. Initially, the focus was coverage of structured and semi-structured indoor areas, but with time and development of better sensors and introduction of GPS, the focus has turned to outdoor coverage. Due to the unstructured nature of an outdoor environment, covering an outdoor area with all its obstacles and simultaneously performing reliable localization is a difficult task. In this paper, two path planning algorithms suitable for solving outdoor coverage tasks are introduced. The algorithms take into account the kinematic constraints of an under-actuated car-like vehicle, minimize trajectory curvatures, and dynamically avoid detected obstacles in the vicinity, all in real-time. We demonstrate the performance of the coverage algorithm in the field by achieving 95% coverage using an autonomous tractor mower without the aid of any absolute localization system or constraints on the physical boundaries of the area.

A lightweight, modular robotic vehicle for the sustainable intensification of agriculture

This paper describes a lightweight, modular and energy efficient robotic vehicle platform designed for broadacre agriculture - the Small Robotic Farm Vehicle (SRFV). The current trend in farming is towards increasingly large machines that optimise the individual farmer’s productivity. Instead, the SRFV is designed to promote the sustainable intensification of agriculture by allowing farmers to concentrate on more important farm management tasks. The robot has been designed with a user-centred approach which focuses the outcomes of the project on the needs of the key project stakeholders. In this way user and environmental considerations for broadacre farming have informed the vehicle platform configuration, locomotion, power requirements and chassis construction. The resultant design is a lightweight, modular four-wheeled differential steer vehicle incorporating custom twin in-hub electric drives with emergency brakes. The vehicle is designed for a balance between low soil impact, stability, energy efficiency and traction. The paper includes modelling of the robot’s dynamics during an emergency brake in order to determine the potential for tipping. The vehicle is powered by a selection of energy sources including rechargeable lithium batteries and petrol-electric generators.

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.

Shifting intimacies

Our world is literally and figuratively turning to ‘dust’. This work acknowledges decay and renewal and the transitional, cyclical natures of interrelated ecologies. It also suggests advanced levels of degradation potentially beyond reparation. Dust exists both on and beneath the border of our unaided vision. Dust particles are predominantly forms of disintegrating solids that often become the substance or catalyst of future forms. Like many tiny forms, dust is an often unnoticed residue with ‘planet-size consequences’. (Hanna Holmes 2001) The image depicts an ethereal, backlit body, continually circling and morphing, apparently floating, suggesting endless cycles of birth, life and death and inviting differing states of meditation, exploration, stillness and play. This never ending video work is taken from a large-scale interactive/media artwork created during a six-month research residency in England at the Institute of Contemporary Art London and at Vincent Dance Theatre Sheffield in 2006. It was originally presented on a raised floor screen made of pure white sand at the ICA in London (see). The project involved developing new interaction, engagement and image making strategies for media arts practice, drawing on the application of both kinetic and proprioceptive dance/performance knowledges. The work was further informed by ecological network theory that assesses the systemic implications of private and public actions within bounded systems. The creative methodology was primarily practice-led which fomented the particular qualities of imagery, generated through cross-fertilising embodied knowledge of Dance and Media Arts. This was achieved through extensive workshopping undertaken in theatres, working ‘on the floor’ live, with dancers, props, sound and projection. And eventually of course, all this dust must settle. (Holmes 2001, from Dust Jacket) Holmes, H. 2001, The Secret Life of Dust: From the Cosmos to the Kitchen Counter, the Big Consequences of Little Things, p.3

Histogram matching and global initialization for laser-only SLAM in large unstructured environments

This paper presents an enhanced algorithm for matching laser scan maps using histogram correlations. The histogram representation effectively summarizes a map's salient features such that pairs of maps can be matched efficiently without any prior guess as to their alignment. The histogram matching algorithm has been enhanced in order to work well in outdoor unstructured environments by using entropy metrics, weighted histograms and proper thresholding of quality metrics. Thus our large-scale scan-matching SLAM implementation has a vastly improved ability to close large loops in real-time even when odometry is not available. Our experimental results have demonstrated a successful mapping of the largest area ever mapped to date using only a single laser scanner. We also demonstrate our ability to solve the lost robot problem by localizing a robot to a previously built map without any prior initialization.

Autonomous hot metal carrier: Navigation and manipulation with a 20 tonne industrial vehicle

This paper reports work on the automation of a hot metal carrier, which is a 20 tonne forklift-type vehicle used to move molten metal in aluminium smelters. To achieve efficient vehicle operation, issues of autonomous navigation and materials handling must be addressed. We present our complete system and experiments demonstrating reliable operation. One of the most significant experiments was five-hours of continuous operation where the vehicle travelled over 8 km and conducted 60 load handling operations. Finally, an experiment where the vehicle and autonomous operation were supervised from the other side of the world via a satellite phone network are described.

Superpixel-based appearance change prediction for long-term navigation across seasons

Changing environments pose a serious problem to current robotic systems aiming at long term operation under varying seasons or local weather conditions. This paper is built on our previous work where we propose to learn to predict the changes in an environment. Our key insight is that the occurring scene changes are in part systematic, repeatable and therefore predictable. The goal of our work is to support existing approaches to place recognition by learning how the visual appearance of an environment changes over time and by using this learned knowledge to predict its appearance under different environmental conditions. We describe the general idea of appearance change prediction (ACP) and investigate properties of our novel implementation based on vocabularies of superpixels (SP-ACP). Our previous work showed that the proposed approach significantly improves the performance of SeqSLAM and BRIEF-Gist for place recognition on a subset of the Nordland dataset under extremely different environmental conditions in summer and winter. This paper deepens the understanding of the proposed SP-ACP system and evaluates the influence of its parameters. We present the results of a large-scale experiment on the complete 10 h Nordland dataset and appearance change predictions between different combinations of seasons.

Phobos and deimos on Mars - two autonomous robots for the DLR SpaceBot Cup

In 2013, ten teams from German universities and research institutes participated in a national robot competition called SpaceBot Cup organized by the DLR Space Administration. The robots had one hour to autonomously explore and map a challenging Mars-like environment, find, transport, and manipulate two objects, and navigate back to the landing site. Localization without GPS in an unstructured environment was a major issue as was mobile manipulation and very restricted communication. This paper describes our system of two rovers operating on the ground plus a quadrotor UAV simulating an observing orbiting satellite. We relied on ROS (robot operating system) as the software infrastructure and describe the main ROS components utilized in performing the tasks. Despite (or because of) faults, communication loss and breakdowns, it was a valuable experience with many lessons learned.

Reactive reaching and grasping on a humanoid: Towards closing the action-perception loop on the iCub

We propose a system incorporating a tight integration between computer vision and robot control modules on a complex, high-DOF humanoid robot. Its functionality is showcased by having our iCub humanoid robot pick-up objects from a table in front of it. An important feature is that the system can avoid obstacles - other objects detected in the visual stream - while reaching for the intended target object. Our integration also allows for 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. Furthermore we show that this system can be used both in autonomous and tele-operation scenarios.

Curiosity driven reinforcement learning for motion planning on humanoids

Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.

Knowing

Directed by Alex Proyas, the Knowing is an action-packed science-fiction disaster movie. A well-known Australian director working in Hollywood, Proyas has developed an international reputation for stylised fantasy and science-fiction movies, including the neo-gothic movie The Crow (1994), the complex science-fiction film Dark City (1998), and the adaptation of Isaac Asimov’s sci-fi classic I, Robot (2004) which earned almost US$350 million theatrically worldwide. Knowing was produced for US$50 million and relies heavily upon special effects (including a visually impressive sequence of the world being destroyed) and high-octane action sequences (including a notable plane crash). Knowing’s cast included Australian actors, Rose Byrne and Ben Mendelsohn, and American actor Nicolas Cage. While Knowing received typically poor critical reviews, the movie performed well at the box-office earning over US$183 million worldwide.

Autonomous hot metal carrier

This paper reports work involved with the automation of a Hot Metal Carrier — a 20 tonne forklift-type vehicle used to move molten metal in aluminium smelters. To achieve efficient vehicle operation, issues of autonomous navigation and materials handling must be addressed. We present our complete system and experiments demontrating reliable operation. One of the most significant experiments was five-hours of continuous operation where the vehicle travelled over 8 km and conducted 60 load handling operations. We also describe an experiment where the vehicle and autonomous operation were supervised from the other side of the world via a satellite phone network.

Is the sun too bright in Queensland? An approach to robust outdoor colour beacon detection

Using cameras onboard a robot for detecting a coloured stationary target outdoors is a difficult task. Apart from the complexity of separating the target from the background scenery over different ranges, there are also the inconsistencies with direct and reflected illumination from the sun,clouds, moving and stationary objects. They can vary both the illumination on the target and its colour as perceived by the camera. In this paper, we analyse the effect of environment conditions, range to target, camera settings and image processing on the reported colours of various targets. The analysis indicates the colour space and camera configuration that provide the most consistent colour values over varying environment conditions and ranges. This information is used to develop a detection system that provides range and bearing to detected targets. The system is evaluated over various lighting conditions from bright sunlight, shadows and overcast days and demonstrates robust performance. The accuracy of the system is compared against a laser beacon detector with preliminary results indicating it to be a valuable asset for long-range coloured target detection.

Evaluation of internal navigation sensor suites for underground mining vehicle navigation

This paper describes a series of trials that were done at an underground mine in New South Wales, Australia. Experimental results are presented from the data obtained during the field trials and suitable sensor suites for an autonomous mining vehicle navigation system are evaluated.