scholarly journals A pneumatic random-access memory for controlling soft robots

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254524
Author(s):  
Shane Hoang ◽  
Konstantinos Karydis ◽  
Philip Brisk ◽  
William H. Grover

Pneumatically-actuated soft robots have advantages over traditional rigid robots in many applications. In particular, their flexible bodies and gentle air-powered movements make them more suitable for use around humans and other objects that could be injured or damaged by traditional robots. However, existing systems for controlling soft robots currently require dedicated electromechanical hardware (usually solenoid valves) to maintain the actuation state (expanded or contracted) of each independent actuator. When combined with power, computation, and sensing components, this control hardware adds considerable cost, size, and power demands to the robot, thereby limiting the feasibility of soft robots in many important application areas. In this work, we introduce a pneumatic memory that uses air (not electricity) to set and maintain the states of large numbers of soft robotic actuators without dedicated electromechanical hardware. These pneumatic logic circuits use normally-closed microfluidic valves as transistor-like elements; this enables our circuits to support more complex computational functions than those built from normally-open valves. We demonstrate an eight-bit nonvolatile random-access pneumatic memory (RAM) that can maintain the states of multiple actuators, control both individual actuators and multiple actuators simultaneously using a pneumatic version of time division multiplexing (TDM), and set actuators to any intermediate position using a pneumatic version of analog-to-digital conversion. We perform proof-of-concept experimental testing of our pneumatic RAM by using it to control soft robotic hands playing individual notes, chords, and songs on a piano keyboard. By dramatically reducing the amount of hardware required to control multiple independent actuators in pneumatic soft robots, our pneumatic RAM can accelerate the spread of soft robotic technologies to a wide range of important application areas.

Author(s):  
S. R. Heister ◽  
V. V. Kirichenko

Introduction. The digital representation of received radar signals has provided a wide range of opportunities for their processing. However, the used hardware and software impose some limits on the number of bits and sampling rate of the signal at all conversion and processing stages. These limitations lead to a decrease in the signal-to-interference ratio due to quantization noise introduced by powerful components comprising the received signal (interfering reflections; active noise interference), as well as the attenuation of a low-power reflected signal represented by a limited number of bits. In practice, the amplitude of interfering reflections can exceed that of the signal reflected from the target by a factor of thousands.Aim. In this connection, it is essential to take into account the effect of quantization noise on the signal-tointerference ratio.Materials and methods. The article presents expressions for calculating the power and power spectral density (PSD) of quantization noise, which take into account the value of the least significant bit of an analog-to-digital converter (ADC) and the signal sampling rate. These expressions are verified by simulating 4-, 8- and 16-bit ADCs in the Mathcad environment.Results. Expressions are derived for calculating the quantization noise PSD of interfering reflections, which allows the PSD to be taken into account in the signal-to-interference ratio at the output of the processing chain. In addition, a comparison of decimation options (by discarding and averaging samples) is performed drawing on the estimates of the noise PSD and the signal-to-noise ratio.Conclusion. Recommendations regarding the ADC bit depth and sampling rate for the radar receiver are presented.


Author(s):  
Neha Jain ◽  
Nir Shlezinger ◽  
Yonina C. Eldar ◽  
Anubha Gupta ◽  
Vivek Ashok Bohara

2021 ◽  
Vol 32 (3) ◽  
Author(s):  
Ruo-Shi Dong ◽  
Lei Zhao ◽  
Jia-Jun Qin ◽  
Wen-Tao Zhong ◽  
Yi-Chun Fan ◽  
...  

1993 ◽  
Vol 7 (4) ◽  
pp. 408 ◽  
Author(s):  
James R. Matey ◽  
M.J. Lauterbach

2017 ◽  
Author(s):  
Evgenii S. Kolodeznyi ◽  
Innokenty I. Novikov ◽  
Andrey V. Babichev ◽  
Alexander S. Kurochkin ◽  
Andrey G. Gladyshev ◽  
...  

2021 ◽  
pp. 127440
Author(s):  
Hao Chi ◽  
Qiulin Zhang ◽  
Shuna Yang ◽  
Bo Yang ◽  
Yanrong Zhai ◽  
...  

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