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Mudasar Basha1,2, Munuswamy Siva Kumar1, Mangali Chinna Chinnaiah2,3

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This study introduces a hardware-based multi-robot system for preventing backing crashes in indoor environments. It integrates sensor fusion and adaptive algorithms for enhanced safety and efficiency in real-time applications.

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Area of Science:

  • Robotics and Automation
  • Computer Engineering

Background:

  • Smart indoor robotics services are increasingly used in real-time applications.
  • Ensuring safety, particularly crash prevention, is crucial for multi-robot systems in confined indoor spaces.

Purpose of the Study:

  • To present a versatile, hardware-scheme-based framework for multi-robot backing crash prevention in both static and dynamic indoor environments.
  • To address the computational challenges of integrating multiple control algorithms in real-time robotics.

Main Methods:

  • Utilized sensor fusion for analyzing multi-robot states and orientation.
  • Implemented a novel hardware framework integrating static (Round-Robin scheduling) and dynamic (First-Come, First-Served scheduling) backing crash prevention.
  • Employed Xilinx-based partial reconfiguration to manage multiple algorithms and adaptive cruise control (ACC) for behavioral control.
  • Developed and validated the system using Verilog HDL on a Zynq Field-Programmable Gate Array (FPGA).

Main Results:

  • Successfully demonstrated a multi-robot system capable of preventing backing crashes in diverse indoor scenarios.
  • The hardware-based approach efficiently integrated complex scheduling algorithms (RR, FCFS, ACC) without runtime computational issues.
  • Validated the effectiveness and competence of the proposed framework on an FPGA-based multi-robot platform.

Conclusions:

  • The proposed hardware scheme provides a robust and efficient solution for multi-robot backing crash prevention in smart indoor environments.
  • The integration of sensor fusion, adaptive algorithms, and partial reconfiguration is key to achieving reliable autonomous operation.
  • This framework enhances the safety and operational capabilities of indoor robotics systems.