Towards dynamically stable gait of an 8 degrees of freedom exoskeleton through predictive forward dynamics simulation

Abstract

The main form of mobility for paraplegic patients is by wheelchair. However, not moving the legs comes with adverse health effects. Exoskeletons are one solution to get these patients walking again. One of the aims of exoskeleton research is the complete restoration of locomotion for paraplegic patients. The achieved gait must be stable, safe and comfortable for the patients. Most research goes into exoskeleton devices which require the use of balancing aids. In the form of crutches, these aids help the exoskeleton users to maintain stability. One of the goals is to eliminate the reliance on balance aids and let the robot do most work. Until now only two exoskeletons are able to achieve autonomous dynamically stable gait. The gait generation algorithms used in these device are based on inverse dynamics. trajectories are calculated and closely tracked. The main challenges of inverse dynamics control algorithms are slow and static movement, balance recovery issues or computational complexity. In this research the aim is to achieve autonomous walking without balance aids. The Project MARCH exoskeleton is taken as an example in this case study. This device has 4 actuated degrees of freedom per leg. The exoskeleton is modelled in OpenSim. Using predictive forward dynamic simulations, a gait algorithm is implemented and evaluated. The reflex-based control algorithm is based on proportional-derivative controllers. This control algorithm is implemented in SCONE and is optimized using the Covariance Matrix Adaptation - Evolution Strategy method. A second simulation experiment uses the same method to achieve standing balance. After optimization of the control algorithm, dynamically stable gait patterns emerge. The exoskeleton model shows limit cycle behaviour and is able to walk for at least 30 seconds at a speed of 0.7 m/s. The controller can optimized to reject perturbations up to 300 N for 0.1 s. The emerging gait pattern shows two features, which complicate the implementation in the real exoskeleton. The model shows a back-heel rotation during stance phase and hits the joint limits during the liftoff phase. Standing balance is also achieved by a different controller. This research serves as a proof of concept on using SCONE (or more general, predictive forward dynamic simulations) to simulate and test an autonomous exoskeleton. The algorithms are completely feedback controlled require no predefined trajectories. Certain features seen in the emerging gait patterns remain to be resolved. This work demands more research to prevent back-heel rotation, to avoid approaching the joint limits and model the toe-off more adequately in order to reduce the peak torque. Furthermore, interesting research can be done on a randomized perturbation rejection and on how to model the inelastic collision at the joint-ends properly as well as on making a comparison between the gait patterns presented in this research and the patterns currently used in the exoskeleton. Only if these challenges are addressed, the gait algorithm becomes eligible to employ in a real exoskeleton.