Robotic Phantom Knee and Digital Twin Platform for Early-Stage Exoskeleton Validation: Nonlinear Stiffness and Tuneable Soft-Tissue Compliance

Abstract

Testing wearable exoskeletons and rehabilitation devices directly on human subjects is often expensive, time-consuming, and ethically challenging, especially during early-stage development when designs and control strategies are frequently updated. In addition, human trials introduce variability that complicates systematic benchmarking and controller verification. This thesis presents the design and development of a robotic phantom knee (RPK) testbed and a real-time Unity-based digital twin (DT) intended to provide a controlled bench-top platform for evaluating exoskeleton controllers. The developed system combines a single-degree-of-freedom knee hinge with antagonistic actuation using two Koala BEAR linear actuators coupled through an elastic linkage to shape the joint’s passive resistance. To emulate tuneable soft-tissue and interface compliance, the platform integrates inflatable chambers whose stiffness is adjusted via closed-loop pressure regulation using solenoid valves, a pump and reservoir, and pressure sensing. A modular ROS 2 (Jazzy) software stack on a Raspberry Pi 5 handles hardware communication, pressure control, and safety functions (including E-STOP and release-air). A Unity-based DT connects via ROS–TCP to visualize system state in real time and support interactive parameter tuning and experiment execution. Subsystem validation demonstrated stable ROS–TCP streaming, safety handling (ESTOP and release-air), and closed-loop pressure tracking suitable for bench-top trials. The platform supports systematic controller development and validation before human testing by enabling controlled experiments under configurable compliance conditions.