In this study, mechanical design and control of a novel parallel elastically actuated (PEA) legged robot are presented. Motion under analysis is limited to vertical apex to apex hopping. Robot is composed of a symmetric four link mechanism as the leg, a brushless direct-drive DC motor and a wrapping cam with extension spring. Controller is based on templates (the simplest model) and anchors (more realistic model) scheme, where the template is the Spring Loaded Inverted Pendulum (SLIP) including a viscous damper which is virtually tunable. For a desired apex, required damping constant is calculated to provide necessary energy to SLIP from an approximate analytical map. Template motion is realized in the anchor model by equating its dynamics to the template dynamics through torque control to equate energy inputs and a wrapping cam to equate potential energies. During the motion, a string is wrapped around a cam by relative motion between two links of the four link mechanism. The string pulls the spring and creates a nonlinear elongation function. Desired elongation is obtained from the required template potential energy and the necessary cam profile is calculated analytically. Thus, a linear compression spring is realized with a tension spring with cam. Static force experiments are performed to show that cam works as desired. Overall simulations and details of mechanical design are presented. This novel PEA robot architecture provides an accurate and energy efficient solution with a simple mechanical design.
Unmanned Surface Vehicles (USV) have gained significant attention in military, science, and research applications in recent years. The development of new USV systems and increased application domain of these platforms has necessitated the development of new motion planning methods to improve the autonomy level of USVs and provide safe and robust navigation across unpredictable marine environments. This study proposes a feedback motion planning and control methodology for dynamic fully-and underactuated USV models built on the recently introduced sparse random neighborhood graphs and constrained nonlinear Model Predictive Control (MPC). This approach employs a feedback motion planning strategy based on sparsely connected obstacle-free regions and the sequential composition of MPC policies. The algorithm generates a sparse neighborhood graph consisting of connected rectangular zones in the discrete planning phase. Inside each node (rectangular region), an MPC-based online feedback control policy funnels the USV with nonlinear dynamics from one rectangle to the other in the network, ensuring no constraint violation on state and input variables occurs. We systematically test the proposed algorithms in different simulation scenarios, including an extreme actuator noise scenario, to test the algorithm's validity, effectiveness, and robustness.INDEX TERMS Nonlinear model predictive control, feedback motion planning, sampling-based motion planning, unmanned surface vehicles.
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