AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2022
Vitabile, Salvatore; Franchini, Silvia; Vassallo, Giorgio
An Optimized Architecture for CGA Operations and Its Application to a Simulated Robotic Arm Journal Article
In: Electronics (Switzerland), vol. 11, no. 21, 2022, ISSN: 2079-9292.
Abstract | Links | BibTeX | Tags: Application-specific processors, Clifford algebra, Computational geometry, Conformal geometric algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, grasping, human-like robotic arms, inverse kinematics, Robotics
@article{vitabileOptimizedArchitectureCGA2022,
title = {An Optimized Architecture for CGA Operations and Its Application to a Simulated Robotic Arm},
author = { Salvatore Vitabile and Silvia Franchini and Giorgio Vassallo},
doi = {10.3390/electronics11213508},
issn = {2079-9292},
year = {2022},
date = {2022-01-01},
journal = {Electronics (Switzerland)},
volume = {11},
number = {21},
abstract = {Conformal geometric algebra (CGA) is a new geometric computation tool that is attracting growing attention in many research fields, such as computer graphics, robotics, and computer vision. Regarding the robotic applications, new approaches based on CGA have been proposed to efficiently solve problems as the inverse kinematics and grasping of a robotic arm. The hardware acceleration of CGA operations is required to meet real-time performance requirements in embedded robotic platforms. In this paper, we present a novel embedded coprocessor for accelerating CGA operations in robotic tasks. Two robotic algorithms, namely, inverse kinematics and grasping of a human-arm-like kinematics chain, are used to prove the effectiveness of the proposed approach. The coprocessor natively supports the entire set of CGA operations including both basic operations (products, sums/differences, and unary operations) and complex operations as rigid body motion operations (reflections, rotations, translations, and dilations). The coprocessor prototype is implemented on the Xilinx ML510 development platform as a complete system-on-chip (SoC), integrating both a PowerPC processing core and a CGA coprocessing core on the same Xilinx Virtex-5 FPGA chip. Experimental results show speedups of 78texttimes and 246texttimes for inverse kinematics and grasping algorithms, respectively, with respect to the execution on the PowerPC processor. textcopyright 2022 by the authors.},
keywords = {Application-specific processors, Clifford algebra, Computational geometry, Conformal geometric algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, grasping, human-like robotic arms, inverse kinematics, Robotics},
pubstate = {published},
tppubtype = {article}
}
Conformal geometric algebra (CGA) is a new geometric computation tool that is attracting growing attention in many research fields, such as computer graphics, robotics, and computer vision. Regarding the robotic applications, new approaches based on CGA have been proposed to efficiently solve problems as the inverse kinematics and grasping of a robotic arm. The hardware acceleration of CGA operations is required to meet real-time performance requirements in embedded robotic platforms. In this paper, we present a novel embedded coprocessor for accelerating CGA operations in robotic tasks. Two robotic algorithms, namely, inverse kinematics and grasping of a human-arm-like kinematics chain, are used to prove the effectiveness of the proposed approach. The coprocessor natively supports the entire set of CGA operations including both basic operations (products, sums/differences, and unary operations) and complex operations as rigid body motion operations (reflections, rotations, translations, and dilations). The coprocessor prototype is implemented on the Xilinx ML510 development platform as a complete system-on-chip (SoC), integrating both a PowerPC processing core and a CGA coprocessing core on the same Xilinx Virtex-5 FPGA chip. Experimental results show speedups of 78texttimes and 246texttimes for inverse kinematics and grasping algorithms, respectively, with respect to the execution on the PowerPC processor. textcopyright 2022 by the authors.
Vitabile, Salvatore; Franchini, Silvia; Vassallo, Giorgio
An Optimized Architecture for CGA Operations and Its Application to a Simulated Robotic Arm Journal Article
In: Electronics (Switzerland), vol. 11, no. 21, 2022, ISSN: 2079-9292.
Abstract | Links | BibTeX | Tags: Application-specific processors, Clifford algebra, Computational geometry, Conformal geometric algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, grasping, human-like robotic arms, inverse kinematics, Robotics
@article{vitabile_optimized_2022,
title = {An Optimized Architecture for CGA Operations and Its Application to a Simulated Robotic Arm},
author = {Salvatore Vitabile and Silvia Franchini and Giorgio Vassallo},
doi = {10.3390/electronics11213508},
issn = {2079-9292},
year = {2022},
date = {2022-01-01},
journal = {Electronics (Switzerland)},
volume = {11},
number = {21},
abstract = {Conformal geometric algebra (CGA) is a new geometric computation tool that is attracting growing attention in many research fields, such as computer graphics, robotics, and computer vision. Regarding the robotic applications, new approaches based on CGA have been proposed to efficiently solve problems as the inverse kinematics and grasping of a robotic arm. The hardware acceleration of CGA operations is required to meet real-time performance requirements in embedded robotic platforms. In this paper, we present a novel embedded coprocessor for accelerating CGA operations in robotic tasks. Two robotic algorithms, namely, inverse kinematics and grasping of a human-arm-like kinematics chain, are used to prove the effectiveness of the proposed approach. The coprocessor natively supports the entire set of CGA operations including both basic operations (products, sums/differences, and unary operations) and complex operations as rigid body motion operations (reflections, rotations, translations, and dilations). The coprocessor prototype is implemented on the Xilinx ML510 development platform as a complete system-on-chip (SoC), integrating both a PowerPC processing core and a CGA coprocessing core on the same Xilinx Virtex-5 FPGA chip. Experimental results show speedups of 78× and 246× for inverse kinematics and grasping algorithms, respectively, with respect to the execution on the PowerPC processor. © 2022 by the authors.},
keywords = {Application-specific processors, Clifford algebra, Computational geometry, Conformal geometric algebra, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, grasping, human-like robotic arms, inverse kinematics, Robotics},
pubstate = {published},
tppubtype = {article}
}
Conformal geometric algebra (CGA) is a new geometric computation tool that is attracting growing attention in many research fields, such as computer graphics, robotics, and computer vision. Regarding the robotic applications, new approaches based on CGA have been proposed to efficiently solve problems as the inverse kinematics and grasping of a robotic arm. The hardware acceleration of CGA operations is required to meet real-time performance requirements in embedded robotic platforms. In this paper, we present a novel embedded coprocessor for accelerating CGA operations in robotic tasks. Two robotic algorithms, namely, inverse kinematics and grasping of a human-arm-like kinematics chain, are used to prove the effectiveness of the proposed approach. The coprocessor natively supports the entire set of CGA operations including both basic operations (products, sums/differences, and unary operations) and complex operations as rigid body motion operations (reflections, rotations, translations, and dilations). The coprocessor prototype is implemented on the Xilinx ML510 development platform as a complete system-on-chip (SoC), integrating both a PowerPC processing core and a CGA coprocessing core on the same Xilinx Virtex-5 FPGA chip. Experimental results show speedups of 78× and 246× for inverse kinematics and grasping algorithms, respectively, with respect to the execution on the PowerPC processor. © 2022 by the authors.