PROJECT TITLE :
Simulation-Based Design for Wearable Robotic Systems: An Optimization Framework for Enhancing a Standing Long Jump
Goal: Technologies that augment human performance are the main focus of intensive research and development, driven by advances in wearable robotic systems. Success has been restricted by the challenge of understanding human–robot interaction. To handle this challenge, we tend to developed an optimization framework to synthesize a realistic human standing long jump and used the framework to explore how simulated wearable robotic devices would possibly enhance jump performance. Strategies: A planar, five-phase, seven-degree-of-freedom model with physiological torque actuators, which have variable torque capability relying on joint position and velocity, was used to represent human musculoskeletal dynamics. An active augmentation device was modeled as a torque actuator that could apply a single pulse of up to one hundred Nm of extension torque. A passive design was modeled as rotational springs concerning every lower limb joint. Dynamic optimization looked for physiological and device actuation patterns to maximize jump distance. Results: Optimization of the nominal case yielded a 2.27 m jump that captured salient kinematic and kinetic options of human jumps. When the active device was added to the ankle, knee, or hip, jump distance increased to between a pair of.forty nine and a pair of.52 m. Active augmentation of all three joints increased the jump distance to 3.ten m. The passive style increased jump distance to 3.thirty two m by adding torques of 135, 365, and 297 Nm to the ankle, knee, and hip, respectively. Conclusion: Dynamic optimization can be used to simulate a standing long jump and investigate human–robot interaction. Significance: Simulation will aid in the planning of performance-enhancing technologies.
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