The objective of this study is to predict the “Aiming While Standing” and “Aiming While Kneeling” motion tasks for a soldier (human) using a full-body, three dimensional digital human model. The digital human is modeled as a 55 degree of freedom branched mechanism. Six degrees of freedom specify the global position and orientation of the coordinate frame attached to the pelvis of the digital human and 49 degrees of freedom represent the revolute joints which model the human joints and determine the kinematics of the entire digital human. Motion is generated by a multi-objective optimization approach minimizing the mechanical energy and joint discomfort simultaneously. A sequential quadratic programming (SQP) algorithm in SNOPT is used to solve the nonlinear optimization problem. The optimization problem is subject to constraints which represent the limitations of the environment, the digital human model and the motion task. Design variables are the joint angle profiles. All the forces, inertial, gravitational as well as external, are known, except the ground reaction forces. The feasibility of the generation of that arbitrary motion by using the given ground contact areas is ensured by using the well known Zero Moment Point (ZMP) constraint. During the kneeling motion, different parts of the body come in contact and lose contact with the ground which is modeled using a general approach. The ground reaction force on each transient ground contact area is determined using the equations of motion. It is assumed that enough friction exists that allow the human to generate reaction forces as determined by the ZMP constraint. Using these ground reaction forces, the required torques at all joints are calculated by the recursive Lagrangian formulation. Using the given method, we can predict realistic motions for the “Aiming While Standing” and “Aiming While Kneeling” tasks. The optimization approach is able to very well predict the “Natural Point of Aim” which is a well known concept for soldiers. In other words, the approach is able to predict the most comfortable final orientation of the feet on the ground for engaging a specific target. We also predict cases where the orientation of the soldier’s feet are enforced. Many virtual experiments have been conducted by changing the target location in the 3D space, changing the anthropometry of the soldier, adding armor to different joints, changing the variable parameters of the rifle, adding backpack and using different weapons.
Swarm optimization approach to non-stationary physical eld survey problem using a group of autonomous underwater vehicles
