Past three decades witnessed a growing interest in biped walking robots because of their advantageous use in the human environment. However, their control is challenging because of their many DOFs and nonlinearities i...
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Past three decades witnessed a growing interest in biped walking robots because of their advantageous use in the human environment. However, their control is challenging because of their many DOFs and nonlinearities in their dynamics. Offline trajectory generation and the so-called open loop walking is one of the control approaches in the literature. There are various problems involved in this approach, the most pronounced one being the difficulty in tuning the gait parameters. This paper proposes an online fuzzy adaptation scheme for one of the trajectory parameters in the offline generated walking pattern. A fuzzy logic system, represented as a three-layer feed-forward neural network is employed to compute the parameter as a function of time. Fuzzy system parameters are adapted via backpropagation. An on-line tuning algorithm is employed. Virtual torsional springs are attached to the trunk center of the biped. The torques generated by the springs serve as the criteria for the tuning and they help maintaining a stable and a longer walk which is necessary for the on-line tuning process. 3D simulation techniques are employed for a 12-DOF biped robot to test the proposed adaptive method.
Patterned Media Storage (PMS) is one of the promising technologies to overcome the limitations of the conventional magnetic recording. For a high areal density PMS, both inter-track interference (ITI) and inter-symbol...
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We provide a systematic analysis of levels of integration between discrete high-level reasoning and continuous low-level feasibility checks to address hybrid planning problems in robotic applications. We identify four...
Developing transformative pathways for industry's compliance with international climate targets requires model-based insights into how supply- and demand-side measures affect industry, material cycles, global...
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Developing transformative pathways for industry's compliance with international climate targets requires model-based insights into how supply- and demand-side measures affect industry, material cycles, global supply chains, socioeconomic activities, and service provisioning that support societal well-being. We review the recent literature modeling the industrial system in low energy and material demand futures, which mitigates environmental impacts without relying on risky future negative emissions and technological fixes. We identify 77 innovative studies drawing on nine distinct industry modeling traditions. We critically assess system definitions and scopes, biophysical and thermodynamic consistency, granularity and heterogeneity, and operationalization of demand and service provisioning. We find that combined supply- and demand-side measures could reduce current economy-wide material use by 56%, energy use by 40% to 60%, and greenhouse gas emissions by 70% to net zero. We call for strengthened interdisciplinary collaborations between industry modeling traditions and demand-side research to produce more insightful scenarios, and we discuss challenges and recommendations for this emerging field.
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