Persistent navigation and mapping using a biologically inspired SLAM system

The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in one dimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

Simultaneous localisation and mapping from natural landmarks using RatSLAM

This paper describes the current state of RatSLAM, a Simultaneous Localisation and Mapping (SLAM) system based on models of the rodent hippocampus. RatSLAM uses a competitive attractor network to fuse visual and odometry information. Energy packets in the network represent pose hypotheses, which are updated by odometry and can be enhanced or inhibited by visual input. This paper shows the effectiveness of the system in real robot tests in unmodified indoor environments using a learning vision system. Results are shown for two test environments; a large corridor loop and the complete floor of an office building.

Effect of representation size and visual ambiguity on RatSLAM system performance

RatSLAM is a vision-based SLAM system based on extended models of the rodent hippocampus. RatSLAM creates environment representations that can be processed by the experience mapping algorithm to produce maps suitable for goal recall. The experience mapping algorithm also allows RatSLAM to map environments many times larger than could be achieved with a one to one correspondence between the map and environment, by reusing the RatSLAM maps to represent multiple sections of the environment. This paper describes experiments investigating the effects of the environment-representation size ratio and visual ambiguity on mapping and goal navigation performance. The experiments demonstrate that system performance is weakly dependent on either parameter in isolation, but strongly dependent on their joint values.

Control issues for velocity sourced Series Elastic Actuators

The Series Elasic Actuator has been proposed as a method for providing safe force or torque based acutation for robots that interact with humans. In this paper we look at some outstanding issues in the implementation and control of Series Elastic Actuators. The study addresses issues in making the Series Elastic Actuator respond effectively in the presence of physical difficulties such as restriction, using a computation efficient controller. The improvement over previous implementations is achieved by treating the motor as a velocity source to the elastic element, rather than as a torque source.

Fast and robust stereo object recognition for spheres

The paper presents a fast and robust stereo object recognition method. The method is currently unable to identify the rotation of objects. This makes it very good at locating spheres which are rotationally independent. Approximate methods for located non-spherical objects have been developed. Fundamental to the method is that the correspondence problem is solved using information about the dimensions of the object being located. This is in contrast to previous stereo object recognition systems where the scene is first reconstructed by point matching techniques. The method is suitable for real-time application on low-power devices.

Assessing the safety of a velocity sourced series elastic actuator using the head injury criterion

The Velocity Sourced Series Elastic Actuator has been proposed as a method for providing safe force or torque based actuation for robots without compromising the actuator performance. In this paper we assess the safety of Velocity Sourced Series Elastic Actuators by measuring the Head Injury Criterion scores for collisions with a model head. The study makes a comparative analysis against stiff, high impedance actuation using the same motor without the series elastic component, showing that the series elastic component brings about a massive reduction in the chance of head injury. The benefits of a collision detection and safe reaction system are shown to be limited to collisions at low speeds, providing greater interaction comfort but not necessarily contributing to safety from injury.

Biologically inspired visual landmark processing for simultaneous localization and mapping

This paper illustrates a method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM). The method is based loosely on biological principles, using layers of filtering and pooling to create learned templates that correspond to different views of the environment. Rather than using a set of landmarks and reporting range and bearing to the landmark, this system maps views to poses. The challenge is to produce a system that produces the same view for small changes in robot pose, but provides different views for larger changes in pose. The method has been developed to interface with the RatSLAM system, a biologically inspired method of SLAM. The paper describes the method of learning and recalling visual landmarks in detail, and shows the performance of the visual system in real robot tests.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup).

In Bed with Autobiography: Unravelling the Fabric of Gender and Genre in Women’s Bed(room) Confessions

This thesis consists of a confessional narrative, What My Mother Doesn’t Know, and an accompanying exegesis, And Why I Should (Maybe) Tell Her. The creative piece employs the confessional mode as a subversive device in three separate narratives, each of which situates the bed as a site of resistance. The exegesis investigates how this self-disclosure in a domestic space flouts the governing rules of self-representation, specifically: telling the truth, respecting privacy and displaying normalcy. The female confession, I argue, creates an alternative space in women’s autobiography where notions of truth-telling can be undermined, the political dimensions of personal experience can be uncovered and the discourse of normality can be negotiated. In particular, women’s confessions told in, on or about the bed, dismantle the genre’s illusion of self and confirm the representative aspects of women’s experience. Framed within these parameters of power and powerlessness, the exegesis includes textual analyses of Charlotte Perkins Gilman’s The Yellow Wallpaper (1892), Tracey Emin’s My Bed (1999) and Lauren Slater’s Lying (2000), each of which exposes in a bedroom space, the author’s most obscure, intimate and traumatic experiences. Situated firmly within and against the genre’s traditional masculine domain, the exegesis also includes mediations on the creative work that validate the bed as my fabric for confession.

Researching crime and violence: Untold stories from the field

Undertaking empirical research on crime and violence can be a tricky enterprise fraught with ethical, methodological, intellectual and legal implications. This chapter takes readers on a reflective journey through the qualitative methodologies I used to research sex work in Kings Cross, miscarriages of justice, female delinquency, sexual violence, and violence in rural and regional settings over a period of nearly 30 years. Reflecting on these experiences, the chapter explores and analyses the reality of doing qualitative field research, the role of the researcher, the politics of subjectivity, the exercise of power, and the ‘muddiness’ of the research process, which is often overlooked in sanitised accounts of the research process (Byrne-Armstrong, Higgs and Horsfall, 2001; Davies, 2000).

The stewardship paradigm : an enquiry into the ethical obligation associated with being in control of resorces

The resource allocation and utilization discourse is dominated by debates about rights particularly individual property rights and ownership. This is due largely to the philosophic foundations provided by Hobbes and Locke and adopted by Bentham. In our community, though, resources come not merely with rights embedded but also obligations. The relevant laws and equitable principles which give shape to our shared rights and obligations with respect to resources take cognizance not merely of the title to the resource (the proprietary right) but the particular context in which the right is exercised. Moral philosophy regarding resource utilisation has from ancient times taken cognizance of obligations but with ascendance of modernity, the agenda of moral philosophy regarding resources, has been dominated, at least since John Locke, by a preoccupation with property rights; the ethical obligations associated with resource management have been largely ignored. The particular social context has also been ignored. Exploring this applied ethical terrain regarding resource utilisation, this thesis: (1) Revisits the justifications for modem property rights (and in that the exclusion of obligations); (2) Identifies major deficiencies in these justifications and reasons for this; (3) Traces the concept of stewardship as understood in classical Greek writing and in the New Testament, and considers its application in the Patristic period and by Medieval and reformist writers, before turning to investigate its influence on legal and equitable concepts through to the current day; 4) Discusses the nature of the stewardship obligation,maps it and offers a schematic for applying the Stewardship Paradigm to problems arising in daily life; and, (5) Discusses the way in which the Stewardship Paradigm may be applied by, and assists in resolving issues arising from within four dominant philosophic world views: (a) Rawls' social contract theory; (b) Utilitarianism as discussed by Peter Singer; (c) Christianity with particular focus on the theology of Douglas Hall; (d) Feminism particularly as expressed in the ethics of care of Carol Gilligan; and, offers some more general comments about stewardship in the context of an ethically plural community.

Spatial mapping and map exploitation : a bio-inspired engineering perspective

Probabilistic robot mapping techniques can produce high resolution, accurate maps of large indoor and outdoor environments. However, much less progress has been made towards robots using these maps to perform useful functions such as efficient navigation. This paper describes a pragmatic approach to mapping system development that considers not only the map but also the navigation functionality that the map must provide. We pursue this approach within a bio-inspired mapping context, and use esults from robot experiments in indoor and outdoor environments to demonstrate its validity. The research attempts to stimulate new research directions in the field of robot mapping with a proposal for a new approach that has the potential to lead to more complete mapping and navigation systems.