Efficient and Accurate CORDIC Pipelined Architecture Chip Design Based on Binomial Approximation for Biped Robot

• Main Authors: Chung, Rih-Lung, Hsueh, Yen, Chen, Shih-Lun, Abu, Patricia Angela R
• Format: text
• Published: Archīum Ateneo 2022
• Subjects: biped robots; binomial approximation; coordinate rotation digital computer (CORDIC); field programmable gate array (FPGA); inverse kinematics; pipeline; Computer Engineering; Engineering; Robotics
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/340; https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1340&context=discs-faculty-pubs
• Institution: Ateneo De Manila University

Recently, much research has focused on the design of biped robots with stable and smooth walking ability, identical to human beings, and thus, in the coming years, biped robots will accomplish rescue or exploration tasks in challenging environments. To achieve this goal, one of the important problems is to design a chip for real-time calculation of moving length and rotation angle of the biped robot. This paper presents an efficient and accurate coordinate rotation digital computer (CORDIC)-based efficient chip design to calculate the moving length and rotation angle for each step of the biped robot. In a previous work, the hardware cost of the accurate CORDIC-based algorithm of biped robots was primarily limited by the scale-factor architecture. To solve this problem, a binomial approximation was carefully employed for computing the scale-factor. In doing so, the CORDIC-based architecture can achieve similar accuracy but with fewer iterations, thus reducing hardware cost. Hence, incorporating CORDIC-based architecture with binomial approximation, pipelined architecture, and hardware sharing machines, this paper proposes a novel efficient and accurate CORDIC-based chip design by using an iterative pipelining architecture for biped robots. In this design, only low-complexity shift and add operators were used for realizing efficient hardware architecture and achieving the real-time computation of lengths and angles for biped robots. Compared with current designs, this work reduced hardware cost by 7.2%, decreased average errors by 94.5%, and improved average executing performance by 31.5%, when computing ten angles of biped robots.