Study and Analysis of Design Optimization and Synthesis of Robotic ARM

A robot is a mechanical or virtual artificial agent, usually an electro-mechanical machine that is guided by a computer program or electronic circuitry. Robots can be autonomous or semi-autonomous. In this thesis, design optimization strategies and synthesis for robotic arm are studied. In the design process, novel optimization methods have been developed to reduce the mass of the whole robotic arm. The optimization of the robotic arm is conducted at three different levels, with the main objective to minimize the robot mass. At the first level, only the drive-train of the robotic arm is optimized. The design process of a robotic arm is decomposed into selection of components for the drive-train to reduce the weight At the second level, kinematic data is combined with the drive-train in the optimization. For this purpose, a dynamic model of the robot is required. Constraints are formulated on the motors, gearboxes and kinematic performance At the third level, a systematic optimization approach is developed, which contains design variables of structural dimensions, geometric dimensions and drive-train composes. Constraints are formulated on the stiffness and deformation. The stiffness and deformation of the arm are calculated through FEA simulation. The main objective of the thesis is to design optimization and synthesis analysis of robotic arm. The corresponding deflections, stresses and strains for that load will be find out by suing the method of finite element analysis

Variable fidelity modeling as applied to trajectory optimization for a hydraulic backhoe

Modeling, simulation, and optimization play vital roles throughout the engineering design process; however, in many design disciplines the cost of simulation is high, and designers are faced with a tradeoff between the number of alternatives that can be evaluated and the accuracy with which they can be evaluated. In this thesis, a methodology is presented for using models of various levels of fidelity during the optimization process. The intent is to use inexpensive, low-fidelity models with limited accuracy to recognize poor design alternatives and reserve the high-fidelity, accurate, but also expensive models only to characterize the best alternatives. Specifically, by setting a user-defined performance threshold, the optimizer can explore the design space using a low-fidelity model by default, and switch to a higher fidelity model only if the performance threshold is attained. In this manner, the high fidelity model is used only to discern the best solution from the set of good solutions, so that computational resources are conserved until the optimizer is close to the solution. This makes the optimization process more efficient without sacrificing the quality of the solution. The method is illustrated by optimizing the trajectory of a hydraulic backhoe. To characterize the robustness and efficiency of the method, a design space exploration is performed using both the low and high fidelity models, and the optimization problem is solved multiple times using the variable fidelity framework.M.S.Committee Chair: Paredis, Chris; Committee Member: Bras, Bert; Committee Member: Burkhart, Roger; Committee Member: Choi, Seung-Kyu

Simultaneous Optimal Uncertainty Apportionment and Robust Design Optimization of Systems Governed by Ordinary Differential Equations

The inclusion of uncertainty in design is of paramount practical importance because all real-life systems are affected by it. Designs that ignore uncertainty often lead to poor robustness, suboptimal performance, and higher build costs. Treatment of small geometric uncertainty in the context of manufacturing tolerances is a well studied topic. Traditional sequential design methodologies have recently been replaced by concurrent optimal design methodologies where optimal system parameters are simultaneously determined along with optimally allocated tolerances; this allows to reduce manufacturing costs while increasing performance. However, the state of the art approaches remain limited in that they can only treat geometric related uncertainties restricted to be small in magnitude. This work proposes a novel framework to perform robust design optimization concurrently with optimal uncertainty apportionment for dynamical systems governed by ordinary differential equations. The proposed framework considerably expands the capabilities of contemporary methods by enabling the treatment of both geometric and non-geometric uncertainties in a unified manner. Additionally, uncertainties are allowed to be large in magnitude and the governing constitutive relations may be highly nonlinear. In the proposed framework, uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach allows statistical moments of the uncertain system to be explicitly included in the optimization-based design process. The framework formulates design problems as constrained multi-objective optimization problems, thus enabling the characterization of a Pareto optimal trade-off curve that is off-set from the traditional deterministic optimal trade-off curve. The Pareto off-set is shown to be a result of the additional statistical moment information formulated in the objective and constraint relations that account for the system uncertainties. Therefore, the Pareto trade-off curve from the new framework characterizes the entire family of systems within the probability space; consequently, designers are able to produce robust and optimally performing systems at an optimal manufacturing cost. A kinematic tolerance analysis case-study is presented first to illustrate how the proposed methodology can be applied to treat geometric tolerances. A nonlinear vehicle suspension design problem, subject to parametric uncertainty, illustrates the capability of the new framework to produce an optimal design at an optimal manufacturing cost, accounting for the entire family of systems within the associated probability space. This case-study highlights the general nature of the new framework which is capable of optimally allocating uncertainties of multiple types and with large magnitudes in a single calculation

Fast Damage Recovery in Robotics with the T-Resilience Algorithm

Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behaviors that do not use them. We evaluate the T-Resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 minutes, T-Resilience consistently leads to substantially better results than the other approaches

Synthesis of a discrete-action thermo-bimetallic actuator with a tongue

The selection of suitable parameters, by experimental or intuitive processes for snap-through actuation of a bimetallic actuator at a prescribed temperature is an extremely time-consuming task. This paper describes a new methodology for the optimization of a discrete action thermo-bimetallic actuator with a tongue. This methodology makes it possible to solve the optimization task with higher efficiency. The requirement is to find optimal parameters values so that the actuator will make a snap-through at a given temperature. The constrained optimization task was performed using an evolutional algorithm and surrogate modelling and this was coded in Matlab. Functional relationships between the criteria and parameters were not set explicitly, but they were calculated using finite element method, each simulation of which was performed in Abaqus

A Practical Approach for the Auto-tuning of PD Controllers for Robotic Manipulators using Particle Swarm Optimization

An auto-tuning method of PD controllers for robotic manipulators is proposed. This method suggests a practical implementation of the particle swarm optimization technique in order to find optimal gain values achieving the best tracking of a predefined position trajectory. For this purpose, The integral of the absolute error IAE is used as a cost function for the optimization algorithm. The optimization is achieved by performing the desired movement of the robot iteratively and evaluating the cost function for every iteration. Therefor, the necessary constraints that guarantee a safe and stable movement of the robot are defined, which are: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. A constraint handling approach is suggested for the optimization algorithm in order to adapt it to the problem in hand. Finally, the efficiency of the proposed method is verified through a practical experiment on a real robot

Automatic Modeling for Modular Reconfigurable Robotic Systems: Theory and Practice

A modular reconfigurable robot consists of a collection of individual link and joint components that can be assembled into a number of different robot ge-ometries. Compared to a conventional industrial robot with fixed geometry, such a system can provide flexibility to the user to cope with a wide spectru