Implementation of Robotic Navigation Algorithms Using Partial Reconfiguration on Zynq SoC

Abstract

The purpose for this research work is to design a heterogeneous method for adaptive, task-based computer hardware reconfiguration for adaptive mobile robotic applications. The proposed method uses the dynamic reconfiguration technique to modify the navigation system to meet the requirements of various navigation algorithms for accomplishing an assigned task. In this work, we have proposed an efficient architecture that satisfies the requirements with adequate hardware and software resources by dynamically reconfiguring the system. The ability to change resources allows robots to collaborate more easily, to enhance performance and fault tolerance. The method is endorsed with case studies in which a multiple mobile robots are loaded with bitfile and their behaviors are dynamically modified during runtime. This paper also proposes the hardware implementation of navigation algorithms on Zynq SoC, XC7Z020CLG484-1, using Xilinx Vivado 2017.3 to simulate and synthesize the design. Verilog is a real-time hardware description language, which is used to deploy multiple robots. Software Development Kit (SDK) is used to facilitate the generation of integrated software applications for Xilinx embedded processors. One of the important aspects of this work is adopting an FPGA-based robot in service-based applications for assisting humans in their daily lives.

FPGA implementation in mobile robot applications: State of the art review

“…In addition, Basha et al (Basha et al, 2022) address the crucial need to design a method that enables the navigation system to dynamically adapt to different navigation algorithms for accomplishing specific tasks. Their proposed method leverages dynamic reconfiguration and efficient allocation of hardware and software resources to enhance mobile robots' performance and fault tolerance.…”

“…Allowing system modifications during runtime facilitates effective collaboration between robots, thereby improving their ability to assist humans in various service-based applications. This research work (Basha et al, 2022) offers a valuable solution for achieving adaptive and efficient hardware reconfiguration in mobile robotic systems. In addition, the authors present the hardware implementation of navigation algorithms on the Zynq SoC XC7Z020CLG484-1, using Xilinx Vivado 2017.3 for simulation and synthesis.…”