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<p>This research presents the design and implementation of a Multi-Functional Cleaner Robot (MFCR) capable of performing simultaneous sweeping, mopping, and object-handling operations using an integrated 3-DOF robotic arm. The MFCR combines intelligent navigation, obstacle avoidance, and adaptive cleaning control within a compact, low-profile design. Comparative testing with conventional cleaning methods showed a sweeping efficiency of <b>95%</b>, mopping efficiency of <b>93%</b>, and obstacle avoidance success of <b>100%</b>. The robotic arm achieved an <b>83% success rate</b> in object pickup tasks. Experiments were conducted across <b>three surface types (tile</b>,<b> carpet</b>,<b> hardwood)</b> with <b>ten repeated trials per mode</b>, yielding a standard deviation below <b>± 2.5%</b> for cleaning accuracy. The robot’s performance demonstrates a <b>20–25% improvement in area coverage</b> and <b>30% reduction in cleaning time</b> compared with traditional tools. Limitations include partial arm misalignment and moderate power drain during full cycles. Future improvements will focus on optimizing arm control and integrating advanced learning-based motion planning algorithms.</p>

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An intelligent multi-purpose service robot for cleaning and object handling in versatile environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月14日

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  • Ahmed A. Abd Eltwab,
  • Hanan M. Amer,
  • Hala B. Nafea

摘要

This research presents the design and implementation of a Multi-Functional Cleaner Robot (MFCR) capable of performing simultaneous sweeping, mopping, and object-handling operations using an integrated 3-DOF robotic arm. The MFCR combines intelligent navigation, obstacle avoidance, and adaptive cleaning control within a compact, low-profile design. Comparative testing with conventional cleaning methods showed a sweeping efficiency of 95%, mopping efficiency of 93%, and obstacle avoidance success of 100%. The robotic arm achieved an 83% success rate in object pickup tasks. Experiments were conducted across three surface types (tile, carpet, hardwood) with ten repeated trials per mode, yielding a standard deviation below ± 2.5% for cleaning accuracy. The robot’s performance demonstrates a 20–25% improvement in area coverage and 30% reduction in cleaning time compared with traditional tools. Limitations include partial arm misalignment and moderate power drain during full cycles. Future improvements will focus on optimizing arm control and integrating advanced learning-based motion planning algorithms.