Static gesture recognition based precise positioning of 5-DOF robotic arm using FPGA

Raj, M. Deepan; Gogul, I.; Thangaraja, M.; Kumar, V. Sathiesh

doi:10.1109/tima.2017.8064804

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…In addition, due to the efficient integration of embedded processors' intellectual properties (IPs) into a FPGA, the highly sophisticated algorithms with heavy computations required by robotic controllers can be performed by software in an FPGA. Thus, many FPGA-based solutions have been implemented in the field of robotics, such as a static gesture recognition system [21], an algorithm for collision detection between Oriented-Bounding-Boxes (OBBs) [22], and an embedded, robust adaptive controller for mobile robotics [23]. Many different works have shown that FPGA implementation of robotic applications is the best solution for optimum performance.…”

Section: Fpga In Roboticsmentioning

confidence: 99%

“…This scenario has forced the search and development of new alternatives, such as SpinalHDL [20], an open-source high-level called whose goal is to use simple elements (flip-flops, gates, if/case statements) to create a new abstraction level and help the designers to reuse their code. Among their advantages over VHDL and Verilog are the evolving capabilities, the reduction of the code size, the easy type conversions, the loop detection, and that it is free and has a user-friendly IDE.As mentioned previously, robotics is one of the application fields of FPGA [21][22][23]. Typically, the implementation of robot intelligence and controllers in FPGAs provides many advantages, like reliability and fast operation, which allow for better robot control.…”

mentioning

confidence: 99%

“…As mentioned previously, robotics is one of the application fields of FPGA [21][22][23]. Typically, the implementation of robot intelligence and controllers in FPGAs provides many advantages, like reliability and fast operation, which allow for better robot control.…”

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confidence: 99%

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Control System in Open-Source FPGA for a Self-Balancing Robot

Cerezo¹,

Castillo²,

Plaza³

2019

Electronics

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Computing in technological applications is typically performed with software running on general-purpose microprocessors, such as the Computer Processing Unit (CPU), or specific ones, like the Graphical Processing Unit (GPU). Application-Specific Integrated Circuits (ASICs) are an interesting option when speed and reliability are required, but development costs are usually high. Field-Programmable Gate Arrays (FPGA) combine the flexibility of software with the high-speed operation of hardware, and can keep costs low. The dominant FPGA infrastructure is proprietary, but open tools have greatly improved and are a growing trend, from which robotics can benefit. This paper presents a robotics application that was fully developed using open FPGA tools. An inverted pendulum robot was designed, built, and programmed using open FPGA tools, such as IceStudio and the IceZum Alhambra board, which integrates the iCE40HX4K-TQ144 from Lattice. The perception from an inertial sensor is used in a PD control algorithm that commands two DC motors. All the modules were synthesized in an FPGA as a proof of concept. Its experimental validation shows good behavior and performance.

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Section: Fpga In Roboticsmentioning

confidence: 99%

mentioning

confidence: 99%

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Control System in Open-Source FPGA for a Self-Balancing Robot

Cerezo¹,

Castillo²,

Plaza³

2019

Electronics

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FPGA implementation of audio and video processing based on Zedboard

Deulkar¹,

Kolhare²

2020

2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC)

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An FPGA-Based Upper-Limb Rehabilitation Device for Gesture Recognition and Motion Evaluation Using Multi-Task Recurrent Neural Networks

et al. 2022

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Upper-Extremity motor impairment affects millions of Americans due to cerebrovascular incidents, spinal cord injuries, or brain trauma. Current therapy practices used to assist these individuals in regaining motor functionality often require extensive time at rehabilitation facilities with potentially prohibitive travel or financial costs. This work presents a mobile low-cost field programmable gate array (FPGA)-smart rehabilitation system that can be used in home environments. The prototype is a rehabilitation table instrumented with a capacitive sensor array (CSA) to track upper-extremity motions of the user through proximity or touch. In addition, inertial measurement units (IMUs) are placed on the affected upper limb and combined with the CSA data with our sensor fusion signal processing architecture. Motions are classified and evaluated using multi-task convolutional recurrent neural networks with three additional motion quality output classes to personalize recognition based on the particular motor skills of each patient. The prototype achieves above 99% accuracy with 32-bit fixed-point format implementation for recognizing dynamic motions and identifying unnatural characteristics (i.e., tremor or limited flexion and extension) in upper limb motions based on sensor values. The convolutional recurrent neural network (C-RNN) fusion classification network is implemented on a 200 MHz Zynq ZCU104 FPGA using an HLS-based design optimized with pipelining and parallelism techniques and achieves 5.4x speedup compared to ARM ® Cortex-A53 implementation running at an operating frequency of 1.3 GHz. The prototype is also demonstrated to perform the machine learning classification in real-time.

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Static gesture recognition based precise positioning of 5-DOF robotic arm using FPGA

Cited by 4 publications

References 9 publications

Control System in Open-Source FPGA for a Self-Balancing Robot

Control System in Open-Source FPGA for a Self-Balancing Robot

FPGA implementation of audio and video processing based on Zedboard

An FPGA-Based Upper-Limb Rehabilitation Device for Gesture Recognition and Motion Evaluation Using Multi-Task Recurrent Neural Networks

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