Deep learning-based semantic segmentation of human features in bath scrubbing robots

With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing staff.This study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog environment.The investigation focuses on semantic segmentation of human features using deep learning methodologies.Initially,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human models.Subsequently,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human regions.Finally,the network is refined by integrating the spatial feature extraction module and the channel attention module based on PointNet.The results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over union.In conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.

Optimization of fracture reduction robot controller based on improved sparrow algorithm

The accuracy of a fracture reduction robot(FRR)is critical for ensuring the safety of surgery.Improving the repositioning accuracy of a FRR,reducing the error,and realizing a safer and more stable folding motion is critical.To achieve this,a sparrow search algorithm(SSA)based on the Levy flight operator was proposed in this study for self-tuning the robot controller parameters.An inverse kinematic analysis of the FRR was also performed.The robot dynamics model was established using Simulink,and the inverse dynamics controller for the fracture reduction mechanism was designed using the computed torque control method.Both simulation and physical experiments were also performed.The actual motion trajectory of the actuator drive rod and its error with a desired trajectory was obtained through simulation.An optimized Levy-sparrow search algorithm(Levy-SSA)crack reduction robot controller demonstrated an overall reduction of two orders of magnitude in the reduction error,with an average error reduction of 98.74%compared with the traditional unoptimized controller.The Levy-SSA increased the convergence of the crack reduction robot control system to the optimal solution,improved the accuracy of the motion trajectory,and exhibited important implications for robot controller optimization.