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arXiv · 2504.13370

Multi-Sensor Fusion-Based Mobile Manipulator Remote Control for Intelligent Smart Home Assistance

Xiao Jin·Bo Xiao·Huijiang Wang·Wendong Wang·Zhenhua Yu
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Abstract

This paper proposes a wearable-controlled mobile manipulator system for intelligent smart home assistance, integrating MEMS capacitive microphones, IMU sensors, vibration motors, and pressure feedback to enhance human-robot interaction. The wearable device captures forearm muscle activity and converts it into real-time control signals for mobile manipulation. The wearable device achieves an offline classification accuracy of 88.33\%\ across six distinct movement-force classes for hand gestures by using a CNN-LSTM model, while real-world experiments involving five participants yield a practical accuracy of 83.33\%\ with an average system response time of 1.2 seconds. In Human-Robot synergy in navigation and grasping tasks, the robot achieved a 98\%\ task success rate with an average trajectory deviation of only 3.6 cm. Finally, the wearable-controlled mobile manipulator system achieved a 93.3\%\ gripping success rate, a transfer success of 95.6\%\, and a full-task success rate of 91.1\%\ during object grasping and transfer tests, in which a total of 9 object-texture combinations were evaluated. These three experiments' results validate the effectiveness of MEMS-based wearable sensing combined with multi-sensor fusion for reliable and intuitive control of assistive robots in smart home scenarios.

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Xiao Jin, Bo Xiao, Huijiang Wang, Wendong Wang, Zhenhua Yu. 2025-04-17. Multi-Sensor Fusion-Based Mobile Manipulator Remote Control for Intelligent Smart Home Assistance. https://arxiv.org/abs/2504.13370

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