An experimental energy consumption comparison between trajectories generated by using the cart-table model and an optimization approach for the Bioloid robot

The aim of this article is to present a statistical comparison of the electric energy expenditure between two techniques for the generation of gait patterns in biped robots. The first one is to minimize the sum of the squared torques applied to the joints of the robot, and the second one is based on the cart-table model. For the experiments, we measured the energy delivered by the battery of the robot to the servomotors. We applied the two aforementioned methods for three velocities (0.5, 1.0, and 1.3 m/min). Additionally, each combination of method and velocity was performed by the robot 10 times. The energy expenditure for each method was compared by applying the Wilcoxon test. In all comparisons, the value of p was lower than 0.004, indicating that the differences were statistically significant. The optimization approach leads to a reduction in energy expenditure that ranged from 9.16 % to 13.35 %. The conclusion is that all the effort required to implement an approach that requires a complete dynamic model of the robot allows a significant reduction in energy consumption.

Human Gait Prediction with a High DOF Upper Body: A Multi-Objective Optimization of Discomfort and Energy Cost

To predict the 3D walking pattern of a human, a detailed upper body model that includes the spine, shoulders, and neck must be made, which is challenging because of the coupling relations of degrees of freedom (DOF) in these body sections. The objective of this study was to develop a discomfort function for including a high DOF upper body model during walking. A multi-objective optimization (MOO) method was formulated by minimizing dynamic effort (DE) and the discomfort function simultaneously. The discomfort function is defined as the sum of the squares of deviation of joint angles from their neutral angle positions. The neutral angle position is defined as a relaxed human posture without actively applied external forces. The DE is the sum of the joint torque squared. To illustrate the capability of including a high DOF upper body, backward walking is used as an example. By minimizing both DE and the discomfort function, a 3D whole-body model with a high DOF upper body for walking was simulated successfully. The proposed MOO is a promising human performance measure to predict human motion using a high DOF upper body with full range of motion. This has been demonstrated by simulating backward walking, lifting, and ingress motions.

Rhythmic Trajectory Design and Control for Rehabilitative Walking in Patients with Lower Limb Disorder

Wearable robotic systems have been a mechanism which clearly drives the motive of bringing back paraplegics back on their feet as well as executing difficult task beyond human ability. The purpose of this research study is to design and investigate the efficacy of rehabilitative walking in patients with lower limb disorders using oscillators which may commonly be referred to as central pattern generators (CPGs). In order to achieve this, a rhythmic trajectory is designed using Van der Pol oscillators. This rhythmic trajectory commensurates with the movement pattern of the hips and knees for a normal walking gait of humans. The dynamical model of a five-link biped exoskeletal device having four actuated joints is computed with regard to the wearer using Lagrangian principles in the sagittal plane. A feedback linearization control technique is therefore utilized for tracking the rhythmic trajectory to achieve a proper following of the human walking gait. Matlab/Simulink is used to validate this proposed strategy in the presence of uncertainties with a view to implementing it practically in the laboratory with human in the loop. Results show that humans with the aid of the exoskeleton device will possess the ability to track this rhythmic trajectory representing the hip and knee joint movements. The controller proved robust enough against disturbance.