Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed

Mobile robot vavigation using a vision based approach

PhD ThesisThis study addresses the issue of vision based mobile robot navigation in a partially cluttered indoor environment using a mapless navigation strategy. The work focuses on two key problems, namely vision based obstacle avoidance and vision based reactive navigation strategy. The estimation of optical flow plays a key role in vision based obstacle avoidance problems, however the current view is that this technique is too sensitive to noise and distortion under real conditions. Accordingly, practical applications in real time robotics remain scarce. This dissertation presents a novel methodology for vision based obstacle avoidance, using a hybrid architecture. This integrates an appearance-based obstacle detection method into an optical flow architecture based upon a behavioural control strategy that includes a new arbitration module. This enhances the overall performance of conventional optical flow based navigation systems, enabling a robot to successfully move around without experiencing collisions. Behaviour based approaches have become the dominant methodologies for designing control strategies for robot navigation. Two different behaviour based navigation architectures have been proposed for the second problem, using monocular vision as the primary sensor and equipped with a 2-D range finder. Both utilize an accelerated version of the Scale Invariant Feature Transform (SIFT) algorithm. The first architecture employs a qualitative-based control algorithm to steer the robot towards a goal whilst avoiding obstacles, whereas the second employs an intelligent control framework. This allows the components of soft computing to be integrated into the proposed SIFT-based navigation architecture, conserving the same set of behaviours and system structure of the previously defined architecture. The intelligent framework incorporates a novel distance estimation technique using the scale parameters obtained from the SIFT algorithm. The technique employs scale parameters and a corresponding zooming factor as inputs to train a neural network which results in the determination of physical distance. Furthermore a fuzzy controller is designed and integrated into this framework so as to estimate linear velocity, and a neural network based solution is adopted to estimate the steering direction of the robot. As a result, this intelligent iv approach allows the robot to successfully complete its task in a smooth and robust manner without experiencing collision. MS Robotics Studio software was used to simulate the systems, and a modified Pioneer 3-DX mobile robot was used for real-time implementation. Several realistic scenarios were developed and comprehensive experiments conducted to evaluate the performance of the proposed navigation systems. KEY WORDS: Mobile robot navigation using vision, Mapless navigation, Mobile robot architecture, Distance estimation, Vision for obstacle avoidance, Scale Invariant Feature Transforms, Intelligent framework

Olfactory learning, its development and changing role in Honeybee (Apis Mellifera) behaviour

The honeybee (Apis mellifera) is capable of showing a wide variety of cognitive tasks, and can be readily conditioned in the laboratory to specific odours, paired with a sucrose reward, using the proboscis extension reflex (PER) learning paradigm. This thesis aims to establish any differences in the behavioural parameters of this olfactory learning. A strong, repeatable methodology is developed, and this specificity of the learning, tested by training bees to different odours, provides a useful model of other phenomenon important in learning theory, such as overshadowing, blocking, massed and spaced training effects, and habituation. The research also indicates a circadian rhythm in the olfactory learning, which is linked to the field, where food sources are only available during certain periods of the day. A new technique was developed to investigate long term captivity and the effects this has on olfactory learning and homing abilities. In both these different, but crucial, learning criteria, captivity played no significant effect, suggesting that the long term memory of the honeybee is a stable, and not easily disrupted entity. The behavioural and developmental stages of the dynamic honeybee colony were examined, to identify any differences in learning in bees aged 1-24 days old. Bees younger than 15 days of age did not show comparable learning to adult foragers, despite having a fully mature olfactory neural pathway. Similarly, PER learning of different castes was researched, with nurse, guard, forager, and precocial forager bees being studied. The results showed that there exists a heirachy in olfactory learning with nurse and guard bees exhibiting learning lower than foragers and precocious foragers. This suggests the social role of the bee, and the interaction between behavioural maturation within its complex society, is a major determinant of olfactory learning ability. The effects of the season are also examined to see if the levels oflearning are constant over the year. Learning was reduced in the summer months, with an increased learning in the winter, which is related to the available forage and the hive demography. The experiments reported show that by using just one example of bee learning, insights into the mechanisms oflearning and memory can be sought. The olfactory system of the honey bee is particularly well researched, and thus, bees can be easily used as a tool at all levels of enquiry from molecular and cellular studies to behavioural genetics, anatomy and physiology

Coordinated movement of multiple robots in an unknown and cluttered environment

Master'sMASTER OF ENGINEERIN

5G: 2020 and Beyond

The future society would be ushered in a new communication era with the emergence of 5G. 5G would be significantly different, especially, in terms of architecture and operation in comparison with the previous communication generations (4G, 3G...). This book discusses the various aspects of the architecture, operation, possible challenges, and mechanisms to overcome them. Further, it supports users? interac- tion through communication devices relying on Human Bond Communication and COmmunication-NAvigation- SENsing- SErvices (CONASENSE).Topics broadly covered in this book are; • Wireless Innovative System for Dynamically Operating Mega Communications (WISDOM)• Millimeter Waves and Spectrum Management• Cyber Security• Device to Device Communicatio