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Adaptive FPGA-Based Accelerators for Human-Robot Interaction in Indoor Environments.

Mangali Sravanthi1,2, Sravan Kumar Gunturi1, Mangali Chinna Chinnaiah3,4

  • 1Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Aziznagar, Hyderabad 500075, Telangana, India.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
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This summary is machine-generated.

This study enhances human-robot interaction using adaptive field-programmable gate array (FPGA) accelerators for real-time indoor localization and service delivery. The system accurately predicts human posture and robot intentions, improving navigation in confined spaces.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Engineering

Background:

  • Human-robot interaction (HRI) in real-time indoor environments presents challenges for service robots, particularly in predicting human posture and location.
  • Accurate human localization and understanding robot intentions are crucial for seamless service delivery in confined spaces.

Purpose of the Study:

  • To develop an adaptive FPGA-based system for real-time human localization and robot intention estimation in indoor environments.
  • To enhance human-robot implicit communication and navigation capabilities for service robots.

Main Methods:

  • Utilized sensor fusion with PIR and ultrasonic sensors for binary classification of static and adaptive human postures.
  • Implemented adaptive Simultaneous Localization and Mapping (SLAM) for robot task delivery and navigation.
Keywords:
FPGAlocalizationposture recognitionsensor fusionservice robot

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  • Developed VLSI hardware schemes and Verilog HDL for algorithm implementation and FPGA synthesis.

    • Main Results:

    • Successfully demonstrated real-time human posture analysis and localization using sensor fusion.
    • Validated the adaptive SLAM-based triangulation navigation method for robot service delivery.
    • Experimental validation conducted in a hospital environment using a Zed-board-based FPGA Xilinx board.

    Conclusions:

    • The proposed FPGA-accelerated approach effectively addresses real-time human localization and robot intention prediction challenges in HRI.
    • The developed methodologies enable adaptive navigation and implicit communication for service robots in complex indoor settings.
    • The system's performance was validated in a realistic hospital environment, showcasing its practical applicability.