14-12-2012, 02:16 PM
Robotics-based synthesis of human motion
Robotics-based synthesis.rtf (Size: 3.22 MB / Downloads: 40)
ABSTRACT
The synthesis of human motion is a complex procedure that involves accurate reconstruction of move-ment sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research. Task-based methods used in robotics may be leveraged to provide novel musculo-skeletal modeling methods and physiologically accurate performance predictions. In this paper, we pres-ent (i) a new method for the real-time reconstruction of human motion trajectories using direct marker tracking, (ii) a task-driven muscular effort minimization criterion and (iii) new human performance met-rics for dynamic characterization of athletic skills. Dynamic motion reconstruction is achieved through the control of a simulated human model to follow the captured marker trajectories in real-time. The oper-ational space control and real-time simulation provide human dynamics at any configuration of the per-formance. A new criteria of muscular effort minimization has been introduced to analyze human static postures. Extensive motion capture experiments were conducted to validate the new minimization crite-rion. Finally, new human performance metrics were introduced to study in details an athletic skill. These metrics include the effort expenditure and the feasible set of operational space accelerations during the performance of the skill. The dynamic characterization takes into account skeletal kinematics as well as muscle routing kinematics and force generating capacities. The developments draw upon an advanced musculoskeletal modeling platform and a task-oriented framework for the effective integration of biome-chanics and robotics methods.
Introduction
In the field of robotics, the motivation to emulate human move-ment is driven by the proliferation of humanoid robots and the de-sire to endow them with human-like movement characteristics (Nakamura et al., 2003). Inspired by human behaviors, our early work in robot control encoded tasks and diverse constraints into artificial potential fields capturing human-like goal-driven behav-iors (Khatib and Le Maitre, 1978). This concept was later formalized in the task oriented operational space dynamic framework (Khatib, 1986, 1987). More recently, this formulation was extended to address whole-body control of humanoid robots and successfully validated on physical robots (Khatib et al., 2004). The framework provides multi-task prioritized control architecture allowing the simultaneous execution of multiple objectives in a hierarchical manner, analogous to natural human motion (see Fig. 1).
Task dynamic behavior and control
For a given desired whole-body task of a human-like robot, the motion behaviors should be specified to be controlled during the execution of the motion. Hand location, balance, effort minimiza-tion, and obstacle and joint limit avoidance are common choices, but the exhaustive list depends upon the motion to be performed. Considering each behavior as an independent task, the number of degrees of freedom describing each task is typically less than the number of joints in the robot. For these situations, there are multi-ple ways of performing the task. This redundancy is labeled in solutions as the posture space of the task, containing all possible motions that do not affect task performance (Khatib et al., 2004). As such, other tasks may be controlled by selectively choosing the path within the posture space.
In this section, the dynamic model of the task/posture decom-position and the model describing the motion of the subtask within the posture space (Khatib et al., 2004) are reviewed. Combination of these two models provides a control structure that compensates for the dynamics in both spaces, significantly improving perfor-mance and responsiveness for multiple tasks.
Direct marker control framework
The task/posture decomposition used in the operational space method provides an effective method that allows us to represent the dynamics of a simulated human subject in a relevant task space that is complemented by a posture space (7). For an arbi-trary number of tasks, the torque decomposition (13) .
Human muscular effort characterization
The ability of humans to move and coordinate their limbs in the performance of common tasks is remarkable. When holding a hea-vy object or applying a force to the environment through a tool, the arms and body of a skillful human are configured in the most effec-tive fashion for the task. The human selection of specific postures among the infinity of possibilities is the result of a long and com-plex process of learning. Through learning, humans seem to come to discover the properties of their bodies and how best to put them to use when performing a task. Exploiting the body kinematic char-acteristics, humans are effectively using the body mechanical advantage to improve the transmission of the tension of muscles
Experimental validation
A set of motion capture experiments were conducted with sub-jects performing static tasks designed to isolate upper limb reach-ing motion. While seated each subject was instructed to pick up a weight and move it to five different targets and hold a static con-figuration at each target for 4 s. The posture-based muscle effort criterion (22) was then computed. SIMM was used to generate the maximum muscle induced moments. The results of this analy-sis showed that the subject’s chosen configuration was typically within several degrees of the predicted configuration associated with minimizing the computed muscle effort (De Sapio et al., 2006). Fig. 7 depicts the results of the muscle effort computations for one of the subject trials with no weight in hand.