Influence of End-Effector Velocities on Robotic Manipulator Dynamic Performance: An Analytical Approach

2003 ◽  
Author(s):  
ChangHwan Kim ◽  
Alan Bowling
Keyword(s):  
Quadratic Forms ◽  
Dynamic Performance ◽  
Optimal Solution ◽  
Problem Formulation ◽  
Optimization Techniques ◽  
Curve Analysis ◽  
Centrifugal Forces ◽  
End Effector ◽  
Nonconvex Problem ◽  
Torque Curve

This article explores the effect that end-effector velocities have on a nonredundant robotic manipulator’s ability to accelerate its end-effector as well as to apply forces/moments to the environment at the end-effector. The velocity effects considered here are the Coriolis and Centrifugal forces, and the reduction of actuator torque with rotor velocity, as described by the speed-torque curve. Analysis of these effects is accomplished using optimization techniques, where the problem formulation consists of a cost function and constraints which are all purely quadratic forms, yielding a nonconvex problem. An analytical solution, based on the dialytic elimination technique, is developed which guarantees that the globally optimal solution can be found. The PUMA 560 manipulator is used as an example to illustrate this methodology.

Velocity Effects on Robotic Manipulator Dynamic Performance

2006 ◽  
Vol 128 (6) ◽  
pp. 1236-1245 ◽  
Author(s):  
Alan P. Bowling ◽  
ChangHwan Kim
Keyword(s):  
Quadratic Forms ◽  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Dynamic Performance ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
End Effector ◽  
Coriolis Forces ◽  
Torque Curve

This article explores the effect that velocities have on a nonredundant robotic manipulator’s ability to accelerate its end-effector, as well as to apply forces/moments to the environment at the end-effector. This work considers velocity forces, including Coriolis forces, and the reduction of actuator torque with rotor velocity described by the speed-torque curve, at a particular configuration of a manipulator. The focus here is on nonredundant manipulators with as many actuators as degrees-of-freedom. Analysis of the velocity forces is accomplished using optimization techniques, where the optimization problem consists of an objective function and constraints which are all purely quadratic forms, yielding a nonconvex problem. Dialytic elimination is used to find the globally optimal solution to this problem. The proposed method does not use iterative numerical optimization methods. The PUMA 560 manipulator is used as an example to illustrate this methodology. The methodology provides an analytical analysis of the velocity forces which insures that the globally optimal solution to the associated optimization problem is found.

scholarly journals OPTIMAL SOLUTION FOR THE DISTRIBUTION OF BY-PRODUCTS IN DISPOSAL UNIT

2017 ◽  
Vol 5 (4RAST) ◽  
pp. 59-63 ◽  
Author(s):  
Jyothi P ◽  
Vatsala G A ◽  
Radha Gupta
Keyword(s):  
Waste Disposal ◽  
Goal Programming ◽  
Programming Model ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Programming Technique ◽  
Emerging Industries ◽  
Goal Programming Model ◽  
Net Profit ◽  
By Products

In present scenario, Waste disposal unit is one of the emerging industries. The process of collection of wastes, segregation of wastes, recycling the wastes and manufacturing by-products and selling the by-products are the major works are undertaken into consideration.  Any business expectation is to get the profit.  Our study is to formulate goal programming model which helps in maximizing the profit by identifying the deviation of goals in the disposal unit. Goal Programming technique is one of the optimization techniques. Manager of the disposal unit can takes the better decision using the deviation of goals. Pre emptive Goals of the study are (i) minimizing the expenditure of the unit and recycling cost of the wastes ii) boosting the net profit of the unit    iii) Maintaining the supply of by-products to each location within the maximum demand iv) Fulfilling demand of by- products in different locations v) Maintaining the minimum supply of recycled by-products to 5 different locations must be at least one.

Gradient Optimization of Inverse Dynamics for Robotic Manipulator Motion Planning Using Combined Optimal Control

2017 ◽  
Author(s):  
Shangdong Gong ◽  
Redwan Alqasemi ◽  
Rajiv Dubey
Keyword(s):  
Optimal Control ◽  
Motion Planning ◽  
Inverse Dynamics ◽  
Null Space ◽  
Optimal Solution ◽  
Human Motion ◽  
Optimization Techniques ◽  
Redundant Manipulators ◽  
Robot Arm ◽  
Gradient Optimization

Motion planning of redundant manipulators is an active and widely studied area of research. The inverse kinematics problem can be solved using various optimization methods within the null space to avoid joint limits, obstacle constraints, as well as minimize the velocity or maximize the manipulability measure. However, the relation between the torques of the joints and their respective positions can complicate inverse dynamics of redundant systems. It also makes it challenging to optimize cost functions, such as total torque or kinematic energy. In addition, the functional gradient optimization techniques do not achieve an optimal solution for the goal configuration. We present a study on motion planning using optimal control as a pre-process to find optimal pose at the goal position based on the external forces and gravity compensation, and generate a trajectory with optimized torques using the gradient information of the torque function. As a result, we reach an optimal trajectory that can minimize the torque and takes dynamics into consideration. We demonstrate the motion planning for a planar 3-DOF redundant robotic arm and show the results of the optimized trajectory motion. In the simulation, the torque generated by an external force on the end-effector as well as by the motion of every link is made into an integral over the squared torque norm. This technique is expected to take the torque of every joint into consideration and generate better motion that maintains the torques or kinematic energy of the arm in the safe zone. In future work, the trajectories of the redundant manipulators will be optimized to generate more natural motion as in humanoid arm motion. Similar to the human motion strategy, the robot arm is expected to be able to lift weights held by hands, the configuration of the arm is changed along from the initial configuration to a goal configuration. Furthermore, along with weighted least norm (WLN) solutions, the optimization framework will be more adaptive to the dynamic environment. In this paper, we present the development of our methodology, a simulated test and discussion of the results.

Integrated Optimal Design of Planar Mechanisms Using Fuzzy Theories

1989 ◽  
Author(s):  
A. K. Dhingra ◽  
S. S. Rao
Keyword(s):  
Nonlinear Programming ◽  
High Speed ◽  
Integrated Approach ◽  
Problem Formulation ◽  
Optimization Techniques ◽  
Motion Path ◽  
Programming Formulation ◽  
Planar Mechanisms ◽  
Dynamic Synthesis ◽  
Motion Characteristics

Abstract A new integrated approach to the design of high speed planar mechanisms is presented. The resulting nonlinear programming formulation combines both the kinematic and dynamic synthesis aspects of mechanism design. The multiobjective optimization techniques presented in this work facilitate the design of a linkage to meet several kinematic and dynamic design criteria. The method can be used for motion, path, and function generation problems. The nonlinear programming formulation also permits the imposition of constraints to eliminate solutions which possess undesirable kinematic and motion characteristics. To model the vague and imprecise information in the problem formulation, the tools of fuzzy set theory have been used. A method of solving the resulting fuzzy multiobjective problem using mathematical programming techniques is presented. The outlined procedure is expected to be useful in situations where doubt arises about the exactness of permissible values, degree of credibility, and correctness of statements and judgements.

scholarly journals Analysis of Firefly–Fuzzy Hybrid Algorithm for Navigation of Quad-Rotor Unmanned Aerial Vehicle

Inventions ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 48
Author(s):  
Brijesh Patel ◽  
Bhumeshwar Patle
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Fuzzy Controller ◽  
Optimal Solution ◽  
Obstacle Detection ◽  
Optimization Techniques ◽  
Multiple Features ◽  
Hybrid Controller ◽  
Aerial Vehicle ◽  
Single Controller ◽  
The Individual

In the present scenario for the development of the unmanned aerial vehicle (UAV), artificial intelligence plays an important role in path planning and obstacle detection. Due to different environments, it is always a task to achieve the proper moment for achieving the target goal while avoiding obstacles with minimum human interference. To achieve the goal with the avoidance of obstacles, individual optimization techniques with metaheuristic algorithms such as fuzzy, particle swarm optimization (PSO), etc. were implemented in various configurations. However, the optimal solution was not attained. Thus, in order to achieve an optimal solution, a hybrid model was developed by using the firefly algorithm and the fuzzy algorithm, establishing multiple features of the individual controller. The path and time optimization were achieved by a standalone controller and a hybrid firefly–fuzzy controller in different conditions, whereby the results of the controller were validated by simulation and experimental results, highlighting the advantages of the hybrid controller over the single controller.

scholarly journals An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1457
Author(s):  
Avelina Alejo-Reyes ◽  
Erik Cuevas ◽  
Alma Rodríguez ◽  
Abraham Mendoza ◽  
Elias Olivares-Benitez
Keyword(s):  
Supplier Selection ◽  
Search Strategy ◽  
Optimization Problems ◽  
Displacement Vector ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Order Quantity ◽  
Hunting Behavior

Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

Reliability-Based Design Optimization of Robotic System Dynamic Performance

2006 ◽  
Vol 129 (4) ◽  
pp. 449-454 ◽  
Author(s):  
Alan P. Bowling ◽  
John E. Renaud ◽  
Jeremy T. Newkirk ◽  
Neal M. Patel ◽  
Harish Agarwal
Keyword(s):  
Design Optimization ◽  
Reference Point ◽  
High Reliability ◽  
Dynamic Performance ◽  
Performance Measure ◽  
Robotic System ◽  
Reliability Based Design Optimization ◽  
End Effector ◽  
Reliability Based Design ◽  
Reliable Design

In this investigation a robotic system’s dynamic performance is optimized for high reliability under uncertainty. The dynamic capability equations (DCE) allow designers to predict the dynamic performance of a robotic system for a particular configuration and reference point on the end effector (i.e., point design). Here the DCE are used in conjunction with a reliability-based design optimization (RBDO) strategy in order to obtain designs with robust dynamic performance with respect to the end-effector reference point. In this work a unilevel performance measure approach is used to perform RBDO. This is important for the reliable design of robotic systems in which a solution to the DCE is required for each constraint call. The method is illustrated on a robot design problem.

A new metaheuristic unscented Kalman filter for state vector estimation of the induction motor based on Ant Lion optimizer

2018 ◽  
Vol 37 (3) ◽  
pp. 1054-1068 ◽  
Author(s):  
Marouane Rayyam ◽  
Malika Zazi ◽  
Youssef Barradi
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Unscented Kalman Filter ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Content Type ◽  
Ant Lion Optimization ◽  
Ant Lion

PurposeTo improve sensorless control of induction motor using Kalman filtering family, this paper aims to introduce a new metaheuristic optimizer algorithm for online rotor speed and flux estimation.Design/methodology/approachThe main problem with unscented Kalman filter (UKF) observer is its sensibility to the initial values of Q and R. To solve the optimal solution of these matrices, a novel alternative called ant lion optimization (ALO)-UKF is introduced. It is based on the combination of the classical UKF observer and a nature-inspired metaheuristic algorithm, ALO.FindingsSynthesized ALO-UKF has given good results over the famous extended Kalman filter and the classical UKF observer in terms of accuracy and dynamic performance. A comparison between ALO and particle swarm optimization (PSO) was established. Simulations illustrate that ALO recovers rapidly and accurately while PSO has a slower convergence.Originality/valueUsing the proposed approach, tuning the design matrices Q and R in Kalman filtering becomes an easy task with a high degree of accuracy and the constraints of time cost are surmounted. Also, ALO-UKF is an efficient tool to improve estimation performance of states and parameters’ uncertainties of the induction motor. Related optimization technique can be extended to faults monitoring by online identification of their corresponding signatures.

scholarly journals Data-Efficient Design Exploration through Surrogate-Assisted Illumination

2018 ◽  
Vol 26 (3) ◽  
pp. 381-410 ◽  
Author(s):  
Adam Gaier ◽  
Alexander Asteroth ◽  
Jean-Baptiste Mouret
Keyword(s):  
Three Dimensional ◽  
Flow Simulation ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Efficient Design ◽  
Design Exploration ◽  
High Performing ◽  
Airfoil Optimization ◽  
Modeling Techniques ◽  
Illumination Algorithms

Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run, instead of only a single near-optimal solution. Unfortunately, these algorithms currently require a large number of function evaluations, limiting their applicability. In this article, we introduce a new illumination algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate modeling techniques to create a map of the design space according to user-defined features while minimizing the number of fitness evaluations. On a two-dimensional airfoil optimization problem, SAIL produces hundreds of diverse but high-performing designs with several orders of magnitude fewer evaluations than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of producing maps of high-performing designs in realistic three-dimensional aerodynamic tasks with an accurate flow simulation. Data-efficient design exploration with SAIL can help designers understand what is possible, beyond what is optimal, by considering more than pure objective-based optimization.

scholarly journals Particle swarm optimization to produce optimal solution

2018 ◽  
Vol 7 (1.7) ◽  
pp. 210 ◽  
Author(s):  
C Saranya Jothi ◽  
V Usha ◽  
R Nithya
Keyword(s):  
Particle Swarm Optimization ◽  
Test Data ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Optimal Test ◽  
Swarm Optimization ◽  
Test Information ◽  
Fitness Value

Search-Based Software Testing is the utilization of a meta-heuristic improving scan procedure for the programmed age of test information. Particle Swarm Optimization (PSO) is one of those technique. It can be used in testing to generate optimal test data solution based on an objective function that utilises branch coverage as criteria. Software under test is given as input to the algorithm. The problem becomes a minimization problem where our aim is to obtain test data with minimum fitness value. This is called the ideal test information for the given programming under test. PSO algorithm is found to outperform most of the optimization techniques by finding least value for fitness function. The algorithm is applied to various software under tests and checked whether it can produce optimal test data. Parameters are tuned so as to obtain better results.

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