Recursive Newton-Euler dynamics and sensitivity analysis for dynamic motion planning

In this study, sensitivity equations are derived for recursive Newton-Euler dynamics using Denavit-Hartenberg (DH) moving coordinates. The dynamics and sensitivity equations depend on the 3x3 DH rotation matrices. Compared to recursive Lagrangian formulation, which depends on 4x4 DH transformation matrices, the recursive Newton-Euler formulation requires less computational effort. In addition, recursive Newton-Euler dynamics explicitly calculates internal joint forces that are not available in recursive Lagrangian formulation. The proposed formulation can handle both prismatic joints and revolute joints. More importantly, analytical sensitivities provide aid to dynamic motion predictions. Three numerical examples are presented to verify the efficacy of the proposed algorithms including two time-minimization trajectory-planning problems and one gait prediction problem. The example model setup, optimization formulation, and simulation results are presented. All three examples are successfully optimized using the proposed dynamics and sensitivity equations. The predicted kinematic and kinetic profiles are partially validated with the data in the literature. Finally, the recursive Newton-Euler dynamics and sensitivity equations are programmed using C++ with the latest math library (Eigen) for developing a general purpose motion prediction software

Adjustable bipedal gait generation using genetic algorithm optimized fourier series formulation

10.1109/IROS.2006.282077IEEE International Conference on Intelligent Robots and Systems4435-444085RB

Distributed sensing in flexible robotic fins: propulsive force prediction and underwater contact sensing

There is recent biological evidence that the pectoral fins of bluegill sunfish are innervated with nerves that respond to bending, and these fish contact obstacles with their fins. However, it is not known how fin-intrinsic sensing could be used to mediate propulsion and touch in engineered fins. The objective of this thesis is to understand the use of distributed sensing in robotic fins, inspired by bony fish fins, for the prediction of propulsive forces and for the discrimination between fluidic loading and contact loading during underwater touch. The research integrates engineering and biology and builds an understanding of fin-intrinsic sensing through study of swimming fish and robotic models of fish fins and sensors. Multiple studies identify which sensor types, sensor placement locations, and model conditions are best for predicting fin propulsive forces and for predicting the state of contact. Comparisons are made between linear and nonlinear Volterra-series convolution models to represent the mapping from sensory data to forces. Best practices for instrumentation and model selection are extracted for a broad range of swimming conditions on a complex, multi-DOF, flexible fin. This knowledge will guide the development of multi-functional systems to navigate and propel through complex, occluded, underwater environments and for sensing and responding to environmental perturbations and obstacles.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201