Forward and Inverse Dynamics of a Unicycle-Like Mobile Robot

Abstract

In this research work, a new method for solving forward and inverse dynamic problems of mechanical systems having an underactuated structure and subjected to holonomic and/or nonholonomic constraints is developed. The method devised in this paper is based on the combination of the Udwadia-Kalaba Equations with the Underactuation Equivalence Principle. First, an analytical method based on the Udwadia-Kalaba Equations is employed in the paper for handling dynamic and control problems of nonlinear nonholonomic mechanical systems in the same computational framework. Subsequently, the Underactuation Equivalence Principle is used for extending the capabilities of the Udwadia-Kalaba Equations from fully actuated mechanical systems to underactuated mechanical systems. The Underactuation Equivalence Principle represents an efficient method recently developed in the field of classical mechanics. The Underactuation Equivalence Principle is used in this paper for mathematically formalizing the underactuation property of a mechanical system considering a particular set of nonholonomic algebraic constraints defined at the acceleration level. On the other hand, in this study, the Udwadia-Kalaba Equations are analytically reformulated in a mathematical form suitable for treating inverse dynamic problems. By doing so, the Udwadia-Kalaba Equations are employed in conjunction with the Underactuation Equivalence Principle for developing a nonlinear control method based on an inverse dynamic approach. As shown in detail in this investigation, the proposed method can be used for analytically solving in an explicit manner the forward and inverse dynamic problems of several nonholonomic mechanical systems. In particular, the tracking control of the unicycle-like mobile robot is considered in this investigation as a benchmark example. Numerical experiments on the dynamic model of the unicycle-like mobile robot confirm the effectiveness of the nonlinear dynamic and control approaches developed in this work.

Conceptual Design of BCI for Mobile Robot Control

“…The robot's movement was set by the linear velocity value of the left v l and the right v r of the wheel. As for the robot dynamics, aggregated dynamic model ) , (   of the MR can be presented at the phase of the detailed design like in [13] or in the following form:…”

“…Concerning the man-operator subsystem, its model As for the robot dynamics, aggregated dynamic model (ρ, ϕ) of the MR can be presented at the phase of the detailed design like in [13] or in the following form:…”

Adaptive Inverse Deep Reinforcement Lyapunov learning control for a floating wind turbine

“…One of the significant and mostly used ML approaches is deep reinforcement learning (DRL) which applies two actor-critic networks to adjust Markov decision processes according to the maximization of cumulative rewards. Unlike supervised/unsupervised learning, DRL receives samples without labels and just considers reward functions in determination of selected policy and state-action pairs [8], [9]. The advantage of DRL over the other ML methods is application of minimal prior information to reach optimum control [10][11][12][13][14][15].…”

A Comprehensive Performance Evaluation of Different Mobile Manipulators Used as Displaceable 3D Printers of Building Elements for the Construction Industry

“…However, since we use the same robot manipulator, we change the mobile platform to find which one is the most appropriate to be used in the construction site. In particular, we test the following configurations: unicycle, car-like and four wheeled omnidirectional with mecanum wheels [34,35]. We test the performance of the mobile manipulator mounted on the three different platforms in terms of error and effort, when printing several types of building elements.…”