Admittance Control of a 3 Omni-Wheel Robot Interacting with a Sensorimotor Model of the Arm

This paper presents an in silico design and validation framework for the admittance control of a threeomni-wheeled rehabilitation robot. The validation framework includes a recently developed, holistic sensorimotor model of the human arm, serving as a realistic environment for verifying robot control performance. This holistic sensorimotor model integrates hierarchical levels of sensorimotor control, including optimal feedback control, muscle synergies, and musculoskeletal dynamics; therefore, it addresses limitations of conventional spring-damper representations by incorporating realistic neural responses to robotic perturbations. Using this in silico verification framework, a three-omni-wheeled robot employing admittance control is developed and validated in simulation across three experimental scenarios: free movement, velocity-dependent force fields, and constant force control. Simulation results show that the holistic model reproduces human-like adaptive trajectories in velocity-dependent force fields that cannot be captured by conventional impedance-based models, while the robot achieves accurate force regulation with RMS errors of 0.282N and 0.049N in the X and Y directions, respectively, with a settling time of 0.26 seconds. The integrated system successfully validates the effectiveness of admittance control for rehabilitation robotics while demonstrating the importance of realistic human modeling in developing responsive and safe controllers for therapeutic applications.