Digital Twin Modeling And Optimal Control Of Soft-Bodied Robotics Using Reservoir Computing

Soft-bodied robots have become increasingly popular due to their ability to per- form tasks that are difficult or impossible for traditional rigid robots. However, accurately modeling and controlling the movement and behavior of soft robots are very challenging due to their complex and dynamic nature. In recent years, Reservoir Computing has emerged as a promising approach to modeling and controlling soft robots. In this thesis, reservoir computing was used to create a digital twin of soft-bodied robots. Specifically, a digital twin of a spring-mass system was created using echo state network, a popular reservoir computing model. Furthermore, an optimal controller was trained using reservoir computing to drive the spring-mass system to follow a desired trajectory. Extensive simulations were carried out to validate the proposed methods. The results demonstrate the effectiveness of the proposed approach. For example, the digital twin model achieved 2% MAPE and the optimal controller achieved 8.7% MAPE for a 20-node 54-spring system. Index Terms: Deep Learning, Digital Twin Modeling, Echo State Network, Optimal control, Soft Bodied Robotics, Time series predictio

Robotic learning of force-based industrial manipulation tasks

Even with the rapid technological advancements, robots are still not the most comfortable machines to work with. Firstly, due to the separation of the robot and human workspace which imposes an additional financial burden. Secondly, due to the significant re-programming cost in case of changing products, especially in Small and Medium-sized Enterprises (SMEs). Therefore, there is a significant need to reduce the programming efforts required to enable robots to perform various tasks while sharing the same space with a human operator. Hence, the robot must be equipped with a cognitive and perceptual capabilities that facilitate human-robot interaction. Humans use their various senses to perform tasks such as vision, smell and taste. One sensethat plays a significant role in human activity is ’touch’ or ’force’. For example, holding a cup of tea, or making fine adjustments while inserting a key requires haptic information to achieve the task successfully. In all these examples, force and torque data are crucial for the successful completion of the activity. Also, this information implicitly conveys data about contact force, object stiffness, and many others. Hence, a deep understanding of the execution of such events can bridge the gap between humans and robots. This thesis is being directed to equip an industrial robot with the ability to deal with force perceptions and then learn force-based tasks using Learning from Demonstration (LfD).To learn force-based tasks using LfD, it is essential to extract task-relevant features from the force information. Then, knowledge must be extracted and encoded form the task-relevant features. Hence, the captured skills can be reproduced in a new scenario. In this thesis, these elements of LfD were achieved using different approaches based on the demonstrated task. In this thesis, four robotics problems were addressed using LfD framework. The first challenge was to filter out robots’ internal forces (irrelevant signals) using data-driven approach. The second robotics challenge was the recognition of the Contact State (CS) during assembly tasks. To tackle this challenge, a symbolic based approach was proposed, in which a force/torque signals; during demonstrated assembly, the task was encoded as a sequence of symbols. The third challenge was to learn a human-robot co-manipulation task based on LfD. In this case, an ensemble machine learning approach was proposed to capture such a skill. The last challenge in this thesis, was to learn an assembly task by demonstration with the presents of parts geometrical variation. Hence, a new learning approach based on Artificial Potential Field (APF) to learn a Peg-in-Hole (PiH) assembly task which includes no-contact and contact phases. To sum up, this thesis focuses on the use of data-driven approaches to learning force based task in an industrial context. Hence, different machine learning approaches were implemented, developed and evaluated in different scenarios. Then, the performance of these approaches was compared with mathematical modelling based approaches.</div

単層カーボンナノチューブ/ポルフィリン-ポリ酸ランダムネットワークを用いたマテリアルリザバー演算素子 —次世代機械知能への新規アプローチ

In a layman’s term, computation is defined as the execution of a given instruction through a programmable algorithm. History has it that starting from the simplest calculator to the sophisticated von Neumann machine, the above definition has been followed without a flaw. Logical operations for which a human takes a minute long to solve, is a matter of fraction of seconds for these gadgets. But contrastingly, when it comes to critical and analytical thinking that requires learning through observation like the human brain, these powerful machines falter and lag behind. Thus, inspired from the brain’s neural circuit, software models of neural networks (NN) integrated with high-speed supercomputers were developed as an alternative tool to implement machine intelligent tasks of function optimization, pattern, and voice recognition. But as device downscaling and transistor performance approaches the constant regime of Moore’s law due to high CMOS fabrication cost and large tunneling energy loss, training these algorithms over multiple hidden layers is turning out to be a grave concern for future applications. As a result, the interplay between faster performance and low computational power requirement for complex tasks deems highly disproportional. Therefore, alternative in terms of both NN models and conventional Neumann architecture needs to be addressed in today’s age for next-generation machine intelligence systems. Fortunately, through extensive research and studies, unconventional computing using a reservoir based neural network platform, called in-materio reservoir computing (RC) has come to the rescue. In-maerio RC uses physical, biological, chemical, cellular automata and other inanimate dynamical systems as a source of non-linear high dimensional spatio-temporal information processing unit to construct a specific target task. RC not only has a three-layer simplified neural architectural layer, but also imposes a cheap, fast, and simplified optimization of only the readout weights with machine intelligent regression algorithm to construct the supervised objective target via a weighted linear combination of the readouts. Thus, utilizing this idea, herein in this work we report such an in-materio RC with a dynamical random network of single walled carbon nanotube/porphyrin-polyoxometalate (SWNT/Por-POM) device. We begin with Chapter 1, which deals with the introduction covering the literature of ANN evolution and the shortcomings of von Neumann architecture and training models of these ANN, which leads us to adopt the in-materio RC architecture. We design the problem statement focused on extending the theoretical RC model of previously suggested SWNT/POM network to an experimental one and present the objective of fabricating a random network based on nanomaterials as they closely resemble the network structure of the brain. Finally, we conclude by stating the scope of this research work aiming towards validating the non-linear high dimensional reservoir property SWNT/Por-POM holds for it to explicitly demonstrate the RC benchmark tasks of optimization and classification. Chapter 2 describes the methodology including the chemical repository required for the facile synthesis of the material. The synthesis part is divided broadly into SWNT purification and then its dispersion with Por-POM to form the desired complex. It is then followed up with the microelectrode array fabrication and the consequent wet-transfer thin film deposition to give the ultimate reservoir architecture of input-output control read pads with SWNT/Por-POM reservoir. Finally we give a briefing of AFM, UV-Vis spectroscopy, FE-SEM characterization techniques of SWNT/Por-POM complex along with the electrical set-up interfaced with software algorithm to demonstrate the RC approach of in-materio machine intelligence. In Chapter 3, we study the current dynamics as a function of voltage and time and validate the non-linear information processing ability intrinsic to the device. The study reveals that the negative differential resistance (NDR) arising from redox nature of Por-POM results in oscillating random noise outputs giving rise to 1/f brain-like spatio-temporal information. We compute the memory capacity (MC) and prove that the device exhibits echo state property of fading memory, but remembers very little of the past information. The low MC and high non-linearity allowed us to choose mostly non-linear tasks of waveform generation, Boolean logic optimization and one-hot vector binary object classification as the RC benchmark. The Chapter 4 relates to the waveform generation task. Utilizing the high dimensional voltage readouts of varying amplitude, phase and higher harmonic frequencies, relative to input sine wave, a regression optimization was performed towards constructing cosine, triangular, square and sawtooth waves resulting in a high accuracy of around 95%. The task complexity of function optimization was further enhanced in Chapter 5 where two inputs were used to construct Boolean logic functions of OR, AND, XOR, NOR, NAND and XNOR. Similar to the waveform, accuracy over 95% could be achieved due to the presence of NDR nonlinearity. Furthermore, the device was also tested for classification problem in Chapter 6. Here we showed an off-line binary classification of four object toys; hedgehog, dog, block and bus, using the grasped tactile information of these objects as inputs obtained from the Toyota Human Support Robot. A one-ridge regression analysis to fit the hot vector supervised target was used to optimize the output weights for predicting the correct outcome. All the objects were successfully classified owing to the 1/f information processing factor. Lastly, we conclude the section in Chapter 7 with the future scope of extending the idea to fabricate a 3-D model of the same material as it opens up opportunity for higher memory capacity fruitful for future benchmark tasks of time-series prediction. Overall, our research marks a step stone in utilizing SWNT/Por-POM as the in-materio RC for the very first time thereby making it a desirable candidate for next-generation machine intelligence.九州工業大学博士学位論文 学位記番号：生工博甲第425号 学位授与年月日：令和3年12月27日1 Introduction and Literature review|2 Methodology|3 Reservoir dynamics emerging from an incidental structure of single-walled carbon nanotube/porphyrin-polyoxometalate complex|4 Fourier transform waveforms via in-materio reservoir computing from single-walled carbon nanotube/porphyrin-polyoxometalate complex|5 Room temperature demonstration of in-materio reservoir computing for optimizing Boolean function with single-walled carbon nanotube/porphyrin-polyoxometalate composite|6 Binary object classification with tactile sensory input information of via single-walled carbon nanotube/porphyrin-polyoxometalate network as in-materio reservoir computing|7 Future scope and Conclusion九州工業大学令和3年

Technological innovation for digital supply chains within small and medium size manufacturing enterprises

The rapidly growing world of digitalisation opens the doorway for the new era of automation that plays a crucial role within the industry. Furthermore, technological innovations that are emerging every day are disrupting traditional business processes especially within small and medium size manufacturing enterprises (SMEs). The current industrial revolution pioneer for profit maximisation with cost reduction shows a significant refinement in improving sustainability that drives forward digitalisation. Evidence shows that industries have identified digitalisation as a priority in the upcoming years as the global supply chain is equipping itself with the digital world in the current industrial revolution. Economic growth is dependent on SMEs around the world where small and medium size Manufacturing Enterprises (SMMEs) play a vital role in the current competitive world while they are not able to manage their supply chains effectively and efficiently due to a lack of optimisation of digitalisation. They identify that technological innovation is evident for transforming themselves with digital supply chain, while global market leading organisations are positioning themselves with the world of digitalisation to their end consumers in their supply chain utilising technological innovation virtually driving towards a new era of a digital ecosystem. This research aims to investigate the impact of technological innovation to foster and promote digital supply chain within SMMEs. Due to the exploratory nature, this study adopted a case study approach where the data is collected using a semi-structured interview across 4 cases from three various countries. The findings indicate a lack of framework for the digitalisation of supply chains within SMMEs, in addition to a lack of technological innovation and financial constraints that served as limiting factors for digitalisation of supply chain within organisations. Further, a framework has been developed consisting of five elements that have been identified from empirical data as being critical for Digital Supply Chain (DSC) transformation. The theoretical contributions of this research are the identification of problems faced, limitations of technological innovation, and an improved understanding of how digital supply chain transformation can be initiated and achieved in the context of SMMEs. The practical contribution of this study is imbedded in the developed framework in the form of recommended strategies for SMMEs for digitalisation of supply chain