Development of a massage robot based on visiontactile fusion and adaptive force control

With the rapid advancement of robotics, robotic massage systems have shown great potential in ensuring consistent massage effectiveness and providing personalized treatments. However, accurately controlling the force applied during the massage, especially given the variation in muscle stiffness and tissue properties across different body regions, remains a significant challenge. Traditional force control methods typically rely on predefined trajectories or basic feedback systems, which are insufficient in adapting to these variations, often resulting in discomfort or suboptimal therapeutic outcomes. To address this, this paper proposes a robotic massage system with adaptive force control, integrating both vision and tactile sensing. The system combines computer vision-based skeletal recognition with real-time feedback from a six-dimensional force sensor to dynamically adjust the applied force based on variations in tissue stiffness across different body areas. Experimental results demonstrate that this approach optimizes force application, significantly enhancing both the effectiveness and comfort of the massage. This study introduces a vision-tactile fusion approach, offering new insights into overcoming the challenges of diversity and adaptability in current robotic massage systems.