Lower limb exoskeleton trajectory optimization using human-robot integrated simulation

Abstract

During lower limb exoskeleton (LLE)-assisted gait, paraplegic patients maintain balance by using supporting crutches against the ground. However, the use of crutches leads to extra energy expenditure for the LLE wearer, and a positive correlation has been found between mean crutch supporting force and oxygen consumption during LLE-assisted gait. In this thesis, I aimed to find an optimized robot joint trajectory to reduce the use of crutches by constructing a human—robot integrated simulation environment for human behavior during the LLE-assisted gait, represented by upper limb and crutch motion. The robot joint trajectory was parameterized using interpolating polynomials passing certain interpolating points, and an optimization problem was formulated to find the interpolating points that reduce the crutch support force in the constructed simulation environment. The optimized robot joint trajectory was found by solving the optimization problem using a genetic algorithm, and through simulation, the proposed optimized robot joint trajectory was found to decrease mean crutch supporting force by 4.5 N compared to the existing robot joint trajectory. From the optimized trajectory, I found that pushing the ground harder with faster ankle joint velocity at the initial swing can reduce the mean crutch supporting force.