Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Mechanical Engineering | en_US |
| dc.contributor.advisor | Navarro-Alarcon, David (ME) | en_US |
| dc.creator | Zahra, Omar Ibn Elkhatab Abdallah A. E. | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/11714 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Bio-inspired methods for sensorimotor mapping in robotic systems | en_US |
| dcterms.abstract | While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behaviour. Our motor system is characterized by several features that allow the emergence of dexterous fluid movements and adapting to various setups and environments. Such features are evident from the evolving behavior in new born babies through motor babbling till achieving precise directed movements. | en_US |
| dcterms.abstract | One feature responsible for such behavior is the self-organisation of the neurons proven to rule both the somatosensory and motor maps. Hence, we present the development of a neuro-inspired approach for characterizing sensorimotor relations in robotic systems. The proposed method has self-organizing and associative properties that enable it to autonomously obtain these relations without any prior knowledge of either the motor (e.g. mechanical structure) or perceptual (e.g. sensor calibration) models. Self-organizing topographic properties are used to build both sensory and motor maps, then the associative properties rule the stability and accuracy of the emerging connections between these maps. Compared to previous works, our method introduces a new varying density self-organizing map (VDSOM) that controls the concentration of nodes in regions with large transformation errors without affecting much the computational time. A distortion metric is measured to achieve a self-tuning sensorimotor model that adapts to changes in either motor or sensory models. The obtained sensorimotor maps prove to have less error than conventional self-organizing methods and potential for further development. | en_US |
| dcterms.abstract | Another key feature is the spiking activity of the neurons. Thus, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The SNN consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with inter-inhibitory connections at the output layer. Spiking neurons are modeled as Izhikevich neurons with a synaptic learning rule based on spike timing-dependent plasticity. Feedback data from proprioceptive and exteroceptive sensors are encoded and fed into the input layer through a motor babbling process. A guideline for tuning the network parameters is proposed and applied along with the particle swarm optimization technique. Our proposed control architecture takes advantage of biologically plausible tools of an SNN to achieve the target reaching task while minimizing deviations from the desired path, and consequently minimizing the execution time. Thanks to the chosen architecture and optimization of the parameters, the number of neurons and the amount of data required for training are considerably low. The SNN is capable of handling noisy sensor readings to guide the robot movements in real-time. Experimental results are presented to validate the control methodology with a vision-guided robot. | en_US |
| dcterms.abstract | Different learning modes and mechanisms allow faster and better acquisition of skills as widely studied in humans and many animals. Specific type of neurons, called mirror neurons, is triggered in the same way whenever an action is performed or while observing someone else doing the same action. This suggests that observing others demonstrating specific movements allows to reinforce our motor abilities. Inspired by such ability, a network is proposed to combine the two previously proposed networks are utilized to allow the robot to mirror the demonstrations observed by a teaching agent to its own joint-space to refine its reaching skill. Hence, experiments are conducted to quantify the improvement achieved based for the proposed learning and control scheme. | en_US |
| dcterms.abstract | Finally, to increase the precision of the developed controller we build a computational model for the cerebellum. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepancies from the reference values. While most studies consider providing the teaching signal as error in joint-space, few studies consider the error in task-space and even fewer consider the spiking nature of the cerebellum on the cellular-level. In this study, a detailed cellular-level forward cerebellar model is developed, including modeling of Golgi and Basket cells which are usually neglected in previous studies. To preserve the biological features of the cerebellum in the developed model, a hyperparameter optimization method tunes the network accordingly. The efficiency and biological plausibility of the proposed cerebellar-based controller is then demonstrated under different robotic manipulation tasks reproducing motor behaviour observed in human reaching experiments. | en_US |
| dcterms.extent | xxiv, 137 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2021 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Robots -- Motion | en_US |
| dcterms.LCSH | Robotics | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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