In human-robot interaction scenarios such as medical rehabilitation and cosmetic care, a robot must maintain a stable contact posture normal to the skin surface. Uncertainties from inter-individual differences, variations in working areas, posture changes during the process, and real-time deformation severely limit the robot’s ability to track the flexible curved contours of the human body. To solve this problem, multiple auxiliary coordinate frames are established on the force-motion interaction system, the kinematics as well as the force/torque relationships during the contact are described and analyzed, and the corresponding transformation matrices of the coordinate frames are constructed. By combining Hertzian elastic contact theory with a biomechanical adhesion-friction model, a normal-vector relationship model based on six-axis force information is developed for a rigid spherical end-effector interacting with soft tissue, which is then used to obtain the current normal attitude in real time. To ensure the accuracy of the six-axis force data, a dual compensation scheme integrating secondary gravity compensation with periodic torque-error compensation is implemented. The proposed method enables real-time tracking of the unknown surface normal posture of human soft tissue through force-sensor feedback. Finally, experiments on a facial model, tracking the trajectory from the glabella to the nose tip along the nasal dorsum, are carried out. The results demonstrate that the normal-attitude error remains within 1.12°~3.20°under an impedance controller that regulates a compliant normal force, meaning that the proposed control strategy is effective and is helpful to enhance the adaptability of robots in unknown human working environments.
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For the individual difference of faces which are the operation object of massage robot, the dynamic motion primitives (DMPs) model was used to generalize the posture trajectory and force trajectory. Firstly, in order to improve the learning accuracy of DMPs, the study proposed an optimized teaching strategy. Based on the Mediapipe feature points in the massage area, the similarity between the operating objects was calculated to optimize the learning objects. Secondly, Gaussian mixture regression (GMR) was introduced, and the algorithm integrated multiple massage information to enhance learning ability. Finally, a back-propagation neural network (BPNN) model was constructed to fit the forced term of DMPs algorithm, which fundamentally changes the limitations of the original model. The experiment shows that the average errors of position and attitude of BPNN-DMPs model are reduced by 44.1% and 54.5%, 44.1% and 54.5%, 29.7% and 46.4% respectively, compared with DMPs, MDMPs and SADMPs algorithms without increasing the running time. Gaussian mixture regression can integrate multiple trajectory patterns and the implementation effect of the optimized teaching strategy is significant. Compared with the non-optimized object, the average errors of the position and posture of the face experiment are reduced by 52.3% and 70.2%, and the standard deviation is reduced by 46.3% and 71.1%. The average errors of position, posture and force in the back experiment decrease by 27.7%, 66.7% and 24.1%, and the standard deviation decreases by 25.7%, 54.4% and 44.1%.
In human-robot interaction tasks, where the robot moves tangentially along the skin surface with a specified normal force, stacking and stretching deformations are displayed by the skin ahead of and behind the movement of the end effector. Discomfort, including sensations of compression and pulling on the human body, can be attributed to these deformations. In addition, the anticipated operational trajectory can deviate because of such deformations. Therefore, under stick-slip friction, this paper introduces a three-dimensional skin deformation simulation model.
First, a three-layered mass-spring-damper (MSD) model, representing the mechanical properties of the skin, muscle fat, and bone layers, is established. Considering the tensile, shear, and bending forces, this model describes the skin deformations. Vision processing methods, including filtering, cropping, uniform sampling, and hand-eye calibration, are employed on the point cloud data obtained from the operation area to establish the particle position of the model. Spring-damper elements, comprising springs and dampers parallelly arranged, are used to connect adjacent particles in the MSD model. Combining modulus of elasticity of various tissue layers helps determine the elastic coefficient of the spring. For the damping properties, a damping algorithm that simulates the viscosity of tissues by reducing the velocity of particles is included in the model. After establishing the simulation model, the stick-slip friction mechanisms between a rigid end effector and a flexible skin surface during tangential sliding in real human-robot interaction tasks are investigated from macroscopic and microscopic perspectives. A particle dynamics equation is established based on the positional dynamics constraints and an improved Kelvin-Voigt dynamic model to facilitate dynamic model simulation under the stick-slip friction. The semi-implicit Euler method is finally employed to solve for the particle position information. The particles of the model are fitted using a cubic spline interpolation surface to obtain three-dimensional deformation information of the skin under various operational environments.
Based on the skin-stretch measurement experimental data, the model displayed vertical and horizontal displacement errors of 0.157 and 0.562 mm, respectively, during a reciprocating linear sliding process with a 17.6 mm travel distance. A robotic arm massage experiment platform and a measurement vision system for skin surface deformation, measuring the stretch deformation of the forearm and the stacking deformation of the upper arm were established to further verify the accuracy of the model. The model simulation produced stretch deformation with average errors of 0.295 and 0.360 mm on the X-axis of tangential movement and the Z-axis of normal loading, respectively, revealing standard deviations of 0.164 and 0.085 mm. The stacking deformation exhibited average errors of 0.317, 0.248, and 0.471 mm in the X, Y, and Z-directions, respectively, revealing standard deviations of 0.090, 0, and 0.232 mm, respectively.
The proposed simulation model demonstrates minimal error and high stability, enabling an accurate simulation of three-dimensional skin deformations due to various working environments in human-robot interaction tasks. Important references for parameter selection and online trajectory planning and control can be obtained using this model to enhance the comfortable operation experience.
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