Task partitioning for foraging robot swarms based on penalty and reward

This thesis is concerned with foraging robots that are retrieving items to a destination using odometry for navigation in enclosed environments, and their susceptibility to dead-reckoning noise. Such noise causes the location of targets recorded by the robots to appear to change over time, thus reducing the ability of the robots to return to the same location. Previous work on task partitioning was attempted in an effort to decrease this error and increase the rate of item collection by making the robots travel shorter distances. \par % Dynamic Partitioning Strategy (DPS) is introduced and explored in this thesis which adjusts the travelling distance from the items location to a collection point as the robots locate the items, through the use of a penalty and reward mechanism. Robots adapt according to their dead-reckoning error rates, where the probability of finding items is related to the ratio between the penalty and the reward parameters. \par % In addition, the diversity in the degrees of error within the members of a robot swarm and the performance repercussion in task partitioning foraging tasks is explored. This is achieved by following an experimental framework composed of three stages: emulation, simulation and hardware. An emulation is generated from an ensemble of machine learning techniques. The emulator allows to perform enriched analyses of simulations of the swarm from a global perspective in a relatively low time compared with experiments in simulations and hardware. Experiments with simulation and hardware provide the contribution of each robot in the swarm to the task

A study of error diversity in robotic swarms for task partitioning in foraging tasks

Often in swarm robotics, an assumption is made that all robots in the swarm behave the same and will have a similar (if not the same) error model. However, in reality this is not the case and this lack of uniformity in the error model, and other operations, can lead to various emergent behaviours. This paper considers the impact of the error model and compares robots in a swarm that operate using the same error model (uniform error) against each robot in the swarm having a different error model (thus introducing error diversity). Experiments are presented in the context of a foraging task. Simulation and physical experimental results show the importance of the error model and diversity in achieving expected swarm behaviour

Evolution of Diverse, Manufacturable Robot Body Plans

Advances in rapid prototyping have opened up new avenues of research within Evolutionary Robotics in which not only controllers but also the body plans (morphologies) of robots can evolve in real-time and real-space. However, this also introduces new challenges, in that robot models that can be instantiated from an encoding in simulation might not be manufacturable in practice (due to constraints associated with the 3D printing and/or automated assembly processes). We introduce a representation for evolving (wheeled) robots with a printed plastic skeleton, and evaluate three variants of a novelty-search algorithm in terms of their ability to produce populations of manufacturable but diverse robots. While the set of manufacturable robots discovered represent only a small fraction of the overall search space of all robots, all methods are shown to be capable of generating a diverse population of manufacturable robots that we conjecture is large enough to seed an evolving robotic ecosystem

Hardware Design for Autonomous Robot Evolution

The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of physical robots, in which both the controllers and body plans are evolved. The transition for evolutionary designs from purely simulation environments into the real world creates the possibility for new types of system able to adapt to unknown and changing environments. In this paper, a system for creating robots is introduced in order to allow for their body plans to be designed algorithmically and physically instantiated using the previously introduced Robot Fabricator. This system consists of two types of components. Firstly, \textit{skeleton} parts are created bespoke for each design by 3D printing, allowing the overall shape of the robot to include almost infinite variety. To allow for the shortcomings of 3D printing, the second type of component are \textit{organs} which contain components such as motors and sensors, and can be attached to the skeleton to provide particular functions. Specific organ designs are presented, with discussion of the design challenges for evolutionary robotics in hardware. The Robot Fabricator is extended to allow for robots with joints, and some example body plans shown to demonstrate the diversity possible using this system of robot generation

Wanted dead or alive : high diversity of macroinvertebrates associated with living and ’dead’ Posidonia oceanica matte

The Mediterranean endemic seagrass Posidonia oceanica forms beds characterised by a dense leaf canopy and a thick root-rhizome ‘matte’. Death of P. oceanica shoots leads to exposure of the underlying matte, which can persist for many years, and is termed ‘dead’ matte. Traditionally, dead matte has been regarded as a degraded habitat. To test whether this assumption was true, the motile macroinvertebrates of adjacent living (with shoots) and dead (without shoots) matte of P. oceanica were sampled in four different plots located at the same depth (5–6 m) in Mellieha Bay, Malta (central Mediterranean). The total number of species and abundance were significantly higher (ANOVA; P<0.05 and P<0.01, respectively) in the dead matte than in living P. oceanica matte, despite the presence of the foliar canopy in the latter. Multivariate analysis (MDS) clearly showed two main groups of assemblages, corresponding to the two matte types. The amphipods Leptocheirus guttatus and Maera grossimana, and the polychaete Nereis rava contributed most to the dissimilarity between the two different matte types. Several unique properties of the dead matte contributing to the unexpected higher number of species and abundance of motile macroinvertebrates associated with this habitat are discussed. The findings have important implications for the conservation of bare P. oceanica matte, which has been generally viewed as a habitat of low ecological value.peer-reviewe

Practical Hardware for Evolvable Robots

The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space

Bootstrapping artificial evolution to design robots for autonomous fabrication

A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary Robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain, however this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability

The PrOVIDe Study: sample characteristics

Purpose : The PrOVIDe study aimed to investigate the prevalence of a range of vision problems among people with dementias, aged 60-89 years and to examine the extent to which these conditions are undetected or inappropriately managed. Methods : The study had two stages, a cross-sectional prevalence study followed by qualitative research. In Stage 1, 708 people with dementia (389 living at home and 319 in care homes) had a domiciliary eye examination. The inclusion criteria were people with dementia (any type), aged 60-89 years; individuals lacking mental capacity to provide informed consent to participate required a consultee who could give approval on their behalf. Results : 22 percent reported not having had a test in the last two years: this included 19 participants who had not been tested in the last 10 years. Prevalence of visual impairment (VI) was 32.4% (95% Confidence Intervals (CI) 28.7 to 36.5) and 16.3% (CI 13.5 to 19.6) for the commonly-used criteria for VI of visual acuity (VA) worse than 6/12 and 6/18 respectively. Of those with VI, 44% (VA<6/12) and 47% (VA<6/18) were correctable with up-to-date spectacles. Almost 50% of remaining un-correctable VI (VA<6/12) was associated with cataract, therefore potentially remediable. Conclusions : Almost 50% of VI was correctable with spectacles - more with cataract surgery. The prevalence of VI was similar to the best comparator data on the general population but the emerging study findings suggest that eye care for people with dementia could be enhanced. Department of Health Disclaimer: The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the HS&DR Programme, NIHR, NHS or the Department of Health

Practical hardware for evolvable robots

The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space