Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

High-Throughput Operant Conditioning in Drosophila Larvae

Operant conditioning is the process by which animals learn to associate their own behaviour with positive or negative outcomes, biasing future action selection in order to maximise reward and avoid punishment. It is an important strategy to ensure survival in an ever-changing environment. Although operant conditioning has been observed across vertebrate and invertebrate species, the underlying neural mechanisms are still not fully understood. The Drosophila larva is an excellent model system to study neural circuits, since it is genetically tractable, with a variety of tools available. Although it is quite small, it is capable of a diverse range of behaviours and can achieve complex learning tasks. However, while the mechanisms underlying classical conditioning, where animals learn about the appetitive or aversive qualities of an external sensory cue, have been extensively studied in larvae, it has remained an open question whether they are capable of operant conditioning. This is in part due to the challenges which arise during the training process: in order to train an animal to associate its own actions with their outcomes, the experimenter needs to be able to deliver rewarding or punishing stimuli directly in response to behaviour. In this thesis, I introduce a novel high-throughput tracker suitable for training up to 16 larvae simultaneously. I have developed a customised software for real-time detection of various actions that larvae perform: left and right bend, forward crawl, roll and back-up. Light and heat stimuli can be administered at individual animals with minimal delay, enabling optogenetic or thermogenetic activation of circuits encoding reward or punishment in response to behaviour. Using this system, I show that Drosophila larvae are capable of operant conditioning. Pairing bends to one direction, e.g. the left, with optogenetic activation of a large group of reward-encoding dopaminergic and serotonergic neurons is sufficient to induce a learned preference for bending towards this side after training. I explore whether there are other types of actions which larvae can learn to associate with valence, and introduce a second operant conditioning paradigm, in which larvae modify their behaviour following pairing of the stimulus with forward crawls. To identify new candidate neurons signalling valence in a learning context, I also conduct a classical conditioning screen, in which I pair an odour with optogenetic activation of distinct neuron types covered by different driver lines. While activation of many types of gustatory sensory neurons paired with the odour was insufficient for memory formation, I find that the serotonergic neurons of the brain and the subesophageal zone (SEZ) can induce strong appetitive learning. Finally, I show that activity of serotonergic rather than dopaminergic neurons is sufficient for memory formation in the operant bend direction paradigm, and that operant conditioning is impaired when restricting activation to the serotonergic neurons of the brain and the SEZ. My results suggest a novel role of serotonergic neurons for learning in insects as well as the existence of learning circuits outside of the mushroom body. Different subsets of serotonergic neurons mediate classical and operant conditioning. This works lays a foundation for future studies of the function of serotonin and the mechanisms underlying operant conditioning at both circuit level and cellular level.Gates Cambridge Scholarshi

Circuit motifs for sensory integration, learning, and the initiation of adaptive behavior in Drosophila

Goal-directed behavior is crucial for survival in complex, dynamic environments. It requires the detection of relevant sensory stimuli and the formation of separable neuronal representations. Learning the contingencies of these sensory stimuli with innately positive or negative valent stimuli (reinforcement) forms associations, allowing the former to cue the latter. This yields cue-based predictions to upgrade the behavioral repertoire from reactive to anticipatory. In this thesis, the Trias of sensory integration, learning of contingencies, and the initiation of anticipatory behavior are studied in the framework of the fruit fly Drosophila olfactory pathway and mushroom body, a higher-order brain center for integrating sensory input and coincidence detection using computational network models representing the mushroom body architecture with varying degrees of abstraction. Additionally, simulations of larval locomotion were employed to investigate how the output of the mushroom body relates to behavior and to foster comparability with animal experiments. We showed that inhibitory feedback within the mushroom body produces sparse stimulus representations, increasing the separability of different sensory stimuli. This separability reduced reinforcement generalization in learning experiments through the decreased overlap of stimulus representations. Furthermore, we showed that feedback from the valence-signaling output to the reinforcement-signaling dopaminergic neurons that innervate the mushroom body could explain experimentally observed temporal dynamics of the formation of associations between sensory cues and reinforcement. This supports the hypothesis that dopaminergic neurons encode the difference between predicted and received reinforcement, which in turn drives the learning process. These dopaminergic neurons have also been argued to convey an indirect reinforcement signal in second-order learning experiments. A new sensory cue is paired with an already established one that activates dopaminergic neurons due to its association with the reinforcement. We demonstrated how different pathways for feedforward or feedback input from the mushroom body’s intrinsic or output neurons can provide an indirect reinforcement signal to the dopaminergic neurons. Any direct or indirect association of sensory cues with reinforcement yielded a reinforcement expectation, biasing the fly’s behavioral response towards the approach or avoidance of the respective sensory cue. We then showed that the simulated locomotory behavior of individual animals in a virtual environment depends on the biasing output of the mushroom body. In conclusion, our results contribute to understanding the implementation of mechanisms for separable stimulus representations, postulated key features of associative learning, and the link between MB output and adaptive behavior in the mushroom body and confirm their explanatory power for animal behavior

Utilizing Systematic Design and Shape Memory Alloys to Enhance Actuation of Modular High-Frequency Origami Robots

Shape memory alloys (SMAs) describe a group of smart metallic materials that can be deformed by external magnetic, thermal, or mechanical influence and then returned to a predetermined shape through the cycling of temperature or stress. They have several advantages, such as having excellent mechanical properties, being low cost, and being easily manufactured, while also providing a compact size, completely silent operation, high work density, and requiring less maintenance over time. SMAs can undergo sold-to-solid phase transformations, and it is because of these phase transformations that they can experience shape memory effect (SME); or the ability to recover from a deformed shape to an initially determined shape through the cycling of temperature. However, since SME requires the cycling of temperature to actuate SMAs, the actuation frequency of these materials has been slow for small-scale applications, as actuation speed is limited by the time it takes to transition from a higher temperature (actuated, pre-determined state) to a lower temperature (flexible, reconfigurable state). While SMAs are known to be highly advantageous, their main drawback is that they are one of the slowest actuation methods in the field of origami robotics. SMAs cannot actuate quickly enough cyclically due to the long cooling times required to get from their austenite (higher temperature, actuated, pre-determined state) phase to their martensite (lower temperature, flexible, reconfigurable state) phase. Researchers have attempted to achieve a higher actuation speed in previous projects by using active cooling agents. However, this study investigated the use of SMAs to initiate high-frequency cyclic movement through a small-scale origami fold without an active cooling source. This study used a combination of different system design parameters to mechanically hasten the actuation speed of a folding hinge with no cooling component present. Through only design and a complete understanding of the SMAs, this study achieved consistent and relatively high results (\u3e1.5 Hz) of an actuation speed for a system of this size. This study discovered knowledge regarding the composition, material properties, and actuation limits of SMAs, and a new systematic design method was proposed for creating origami robots

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

An analysis of the locomotory behaviour and functional morphology of errant polychaetes

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Design and Fabrication of Soft 3D Printed Actuators: Expanding Soft Robotics Applications

Soft pneumatic actuators are ideal for soft robotic applications due to their innate compliance and high power-weight ratios. Presently, the majority of soft pneumatic actuators are used to create bending motions, with very few able to produce significant linear movements. Fewer can actively produce strains in multiple directions. The further development of these actuators is limited by their fabrication methods, specifically the lack of suitable stretchable materials for 3D printing. In this thesis, a new highly elastic resin for digital light projection 3D printers, designated ElastAMBER, is developed and evaluated, which shows improvements over previously synthesised elastic resins. It is prepared from a di-functional polyether urethane acrylate oligomer and a blend of two different diluent monomers. ElastAMBER exhibits a viscosity of 1000 mPa.s at 40 °C, allowing easy printing at near room temperatures. The 3D-printed components present an elastomeric behaviour with a maximum extension ratio of 4.02 ± 0.06, an ultimate tensile strength of (1.23 ± 0.09) MPa, low hysteresis, and negligible viscoelastic relaxation