Biologically Inspired Guidance for Autonomous Systems

Animals and humans can perform purposeful actions using only their senses. Birds can perch on branches; bats use echolocation to hunt prey and humans are able to control vehicles. It must therefore be possible for autonomous systems to replicate this autonomous behaviour if an understanding of how animals and humans perceive their environment and guide their movements is obtained. Tau theory offers a potential explanation as to how this is achieved in nature. Tau theory posits, that in combination with the so-called ‘motion guides’, animals and humans perform useful movements by closing action-gaps, i.e. gaps between the current state and a desired state. The theory suggests that the variabl

Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems

Findings in recent years on the sensitivity of convolutional neural networks to additive noise, light conditions and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In an attempt to bring computer vision algorithms closer to the capabilities of a human operator, the mechanisms of the human visual system was analyzed in this work. Recent studies show that the mechanisms behind the recognition process in the human brain include continuous generation of predictions based on prior knowledge of the world. These predictions enable rapid generation of contextual hypotheses that bias the outcome of the recognition process. This mechanism is especially advantageous in situations of uncertainty, when visual input is ambiguous. In addition, the human visual system continuously updates its knowledge about the world based on the gaps between its prediction and the visual feedback. Convolutional neural networks are feed forward in nature and lack such top-down contextual attenuation mechanisms. As a result, although they process massive amounts of visual information during their operation, the information is not transformed into knowledge that can be used to generate contextual predictions and improve their performance. In this work, an architecture was designed that aims to integrate the concepts behind the top-down prediction and learning processes of the human visual system with the state of the art bottom-up object recognition models, e.g., deep convolutional neural networks. The work focuses on two mechanisms of the human visual system: anticipation-driven perception and reinforcement-driven learning. Imitating these top-down mechanisms, together with the state of the art bottom-up feed-forward algorithms, resulted in an accurate, robust, and continuously improving target recognition model

FDIR for a Biologically Inspired Trenchless Drilling Device

Failure Detection, Isolation and Recovery (FDIR) of autonomous systems working in hazardous conditions is essential. Methods of detection and recovery without intervention are required. This work describes the failure modes currently identified with an autonomous biologically inspired trenchless drilling robotic system. Inverse Simulation is used for detecting failures and is demonstrated on a simulation model of the robotic system. Results from the experiments, show that Inverse Simulation can be used to detect and identify system failures

A comparison study of biologically inspired propulsion systems for an autonomous underwater vehicle

The field of Autonomous Underwater Vehicles (AUVs) has increased dramatically in size and scope over the past two decades. Application areas for AUVs are numerous and varied; from deep sea exploration, to pipeline surveillance to mine clearing. However, one limiting factor with the current technology is the duration of missions that can be undertaken and one contributing factor to this is the efficiency of the propulsion system, which is usually based on marine propellers. As fish are highly efficient swimmers greater propulsive efficiency may be possible by mimicking their fish tail propulsion system. The main concept behind this work was therefore to investigate whether a biomimetic fish-like propulsion system is a viable propulsion system for an underwater vehicle and to determine experimentally the efficiency benefits of using such a system. There have been numerous studies into biomimetic fish like propulsion systems and robotic fish in the past with many claims being made as to the benefits of a fish like propulsion system over conventional marine propulsion systems. These claims include increased efficiency and greater manoeuvrability. However, there is little published experimental data to characterise the propulsive efficiency of a fish like propulsive system. Also, very few direct experimental comparisons have been made between biomimetic and conventional propulsion systems. This work attempts to address these issues by directly comparing experimentally a biomimetic underwater propulsion system to a conventional propulsion system to allow for a better understanding of the potential benefits of the biomimetic system. This work is split into three parts. Firstly, the design and development of a novel prototype vehicle called the RoboSalmon is covered. This vehicle has a biomimetic tendon drive propulsion system which utilizes one servo motor for actuation and has a suite of onboard sensors and a data logger. The second part of this work focuses on the development of a mathematical model of the RoboSalmon vehicle to allow for a better understanding of the dynamics of the system. Simulation results from this model are compared to the experimental results and show good correlation. The final part of the work presents the experimental results obtained comparing the RoboSalmon prototype with the biomimetic tail system to the propeller and rudder system. These experiments include a study into the straight swimming performance, recoil motion, start up transients and power consumption. For forward swimming the maximum surge velocity of the RoboSalmon was 0.18ms-1 and at this velocity the biomimetic system was found to be more efficient than the propeller system. When manoeuvring the biomimetic system was found to have a significantly reduced turning radius. The thesis concludes with a discussion of the main findings from each aspect of the work, covering the benefits obtained from using the tendon drive system in terms of efficiencies and manoeuvring performance. The limitations of the system are also discussed and suggestions for further work are included

Mean Field Behaviour of Collaborative Multi-Agent Foragers

Collaborative multi-agent robotic systems where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically-inspired robotics rely on simplifying agents and increasing their number to obtain more efficient solutions to such problems, drawing similarities with natural processes. In this work we focus on the problem of a biologically-inspired multi-agent system solving collaborative foraging. We show how mean field techniques can be used to re-formulate such a stochastic multi-agent problem into a deterministic autonomous system. This de-couples agent dynamics, enabling the computation of limit behaviours and the analysis of optimality guarantees. Furthermore, we analyse how having finite number of agents affects the performance when compared to the mean field limit and we discuss the implications of such limit approximations in this multi-agent system, which have impact on more general collaborative stochastic problems

Biologically-Inspired Concepts for Autonomic Self-Protection in Multiagent Systems

Biologically-inspired autonomous and autonomic systems (AAS) are essentially concerned with creating self-directed and self-managing systems based on metaphors &om nature and the human body, such as the autonomic nervous system. Agent technologies have been identified as a key enabler for engineering autonomy and autonomicity in systems, both in terms of retrofitting into legacy systems and in designing new systems. Handing over responsibility to systems themselves raises concerns for humans with regard to safety and security. This paper reports on the continued investigation into a strand of research on how to engineer self-protection mechanisms into systems to assist in encouraging confidence regarding security when utilizing autonomy and autonomicity. This includes utilizing the apoptosis and quiescence metaphors to potentially provide a self-destruct or self-sleep signal between autonomic agents when needed, and an ALice signal to facilitate self-identification and self-certification between anonymous autonomous agents and systems

Enhancing service-oriented holonic multi-agent systems with self-organization

Multi-agents systems and holonic manufacturing systems are suitable approaches to design a new and alternative class of production control systems, based on the decentralization of control functions over distributed autonomous and cooperative entities. However, in spite of their enormous potential they lack some aspects related to interoperability, migration, optimisation in decentralised structures and truly self-adaptation. This paper discusses the advantages of combining these paradigms with complementary paradigms, such as service-oriented architectures, and enhancing them with biologically inspired algorithms and techniques, such as emergent behaviour and self-organization, to reach a truly robust, agile and adaptive control system. An example of applying a stigmergy-based algorithm to dynamically route pallets in a production system is also provided