Dynamic Simulation for Zero-Gravity Activities

Working and training for space activities is difficult in terrestrial environments. We approach this crucial aspect of space human factors through 3D computer graphics dynamics simulation of crewmembers, their tasks, and physics-based movement modeling. Such virtual crewmembers may be used to design tasks and analyze their physical workload to maximize success and safety without expensive physical mockups or partially realistic neutral-buoyancy tanks. Among the software tools we have developed are methods for fully articulated 3D human models and dynamic simulation. We are developing a fast recursive dynamics algorithm for dynamically simulating articulated 3D human models, which comprises kinematic chains - serial, closed-loop, and tree-structure - as well as the inertial properties of the segments. Motion planning is done by first solving the inverse kinematic problem to generate possible trajectories, and then by solving the resulting nonlinear optimal control problem. For example, the minimization of the torques during a simulation under certain constraints is usually applied and has its origin in the biomechanics literature. Examples of space activities shown are zero-gravity self orientation and ladder traversal. Energy expenditure is computed for the traversal task

Design, analysis and passive balance control of a 7-DOF biped robot

Biped robots have many advantages than traditional wheeled or tracked robots. They have better mobility in rough terrain and can travel on discontinuous path. The legs can also provide an active suspension that decouples the path of the trunk from the paths of the feet. Furthermore, the legs are able to step over considerably bigger obstacles compared to wheeled robots. However, it is difficult to maintain the balance of biped robots because they can easily tip over or slide down. To be able to walk stably, it is necessary for the robot to walk through a proper trajectory, which is the goal of this research. In this research, a complete 7-DOF biped walking trajectory is planned based on human walking trajectory by cubic Hermite interpolation method. The kinematics and dynamic model of the biped are derived by Denavit-Hartenberg (D-H) representation and Euler-Lagrange motion equations, respectively. The zero moment point of the robot is simulated to check the stability of the walking trajectory. The setpoint sampling method and sampling rate for trajectory tracking control are investigated by studying sinusoidal curve tracking on a single link robot arm. Two control sampling time selection methods are introduced for digital controllers. A 7-DOF biped is designed and built for experiments. Each joint has its own independent microcontroller-based control system. PD controllers are used to control the biped joints. Simulations are performed for the walking trajectory and zero moment point. Simulation results show that the walking trajectory is stable for the 7-DOF biped. Experiment results indicate that the sampling time is proper and the PID controller works well in both setpoint control and trajectory tracking. The experiment for the marching in place shows the trajectory is stable and the biped can balance during the marching process

Recursive dynamics and optimal control techniques for human motion planning

Techniques for simulating dynamically correct human movements are becoming increasingly important in medical, military, graphics and space-exploration applications. In this thesis, we develop an efficient optimal control based approach to this problem. We model virtual humans as a kinematic chain consisting of serial, closed-loop, and tree-structures. We first experimented with a Lagrangian approach employing a recursive root finding technique and applied it to the problem of human ladder climbing. However, the Lagrange\u27s method has several limitations: (1) it does not scale well with increasing number of degrees of freedom of a figure model; (2) it is error-prone since the re-formulation of the dynamic equations is necessary for different articulated figures; (3) it is cumbersome in treating complex figure topology. To overcome these limitations and to include knowledge from biomechanical studies, we have developed an efficient minimum-torque motion planning method. This new method is based on the use of optimal control theory within a recursive dynamics framework. Recursive dynamics computation has been shown to allow efficient simulation of systems with large degrees of freedom regardless of their topology. It obviates the need for the reformulation of the dynamic equations for different articulated figures. We then use a quasi-Newton method based nonlinear programming technique to solve our minimal torque-based human motion planning problem. This method achieves superlinear convergence. We use the screw theoretical method to compute analytically the necessary gradient of the motion and force. This provides a better conditioned optimization computation and allows the robust and efficient implementation of our method. Cubic spline functions have been used to make the search space for an optimal solution finite. In addition, our approach is suitable for implementation using an object-oriented programming language. We demonstrate the efficacy of our proposed method based on a variety of human motion tasks involving open and closed loop kinematic chains. Our models are built using parameters chosen from an anthropomorphic database. Simulations are presented to validate our approach. These include a variety of human activity simulations to show the robustness of the method in various environments. The value of the method is justified by the natural looking and the physically correct motions that can be synthesized