Zobrazeno 1 - 5
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pro vyhledávání: '"Mohammadi, Ali"'
Publikováno v:
Mohammadi, A, Sadrafshari, S, Shokrani, A & Bowen, C 2023, Asymmetric Quad-Leg Orthoplanar Spring For Wideband Piezoelectric Micro-Energy Harvesting . in 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS) . IEEE, pp. 697-700, 36th IEEE International Conference on Micro Electro Mechanical Systems (MEMS) 2023, Munich, Germany, 15/01/23 . https://doi.org/10.1109/MEMS49605.2023.10052512
Piezoelectric energy harvesters (EH) generate their highest energy levels at the resonant frequencies of the transducer devices. To provide a wide-band EH solution, nonlinear mechanical resonators with multiple resonant modes can be used. We present
Autor:
Dey, Sharmita, Fan, David, Schmid, Robin, Dixit, Anushri, Otsu, Kyohei, Touma, Thomas, Schilling, Arndt F., Agha-mohammadi, Ali-akbar
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face stability challenges on slippery surfaces. This can be resolved by ad
Publikováno v:
2022 International Conference on Robotics and Automation (ICRA).
A new belief space planning algorithm, called covariance steering Belief RoadMap (CS-BRM), is introduced, which is a multi-query algorithm for motion planning of dynamical systems under simultaneous motion and observation uncertainties. CS-BRM extend
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Monocular depth inference has gained tremendous attention from researchers in recent years and remains as a promising replacement for expensive time-of-flight sensors, but issues with scale acquisition and implementation overhead still plague these s
Publikováno v:
IROS
Agha-mohammad
Agha-mohammad
In persistent missions, taking system’s health and capability degradation into account is an essential factor to predict and avoid failures. The state space in health-aware planning problems is often a mixture of continuous vehicle-level and discre