E. J. Solteiro Pires

Manipulator trajectory planning using a MOEA

Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.

Fractional order dynamics in a GA planner

This work addresses the signal propagation and the fractional-order dynamics during, the evolution of a genetic algorithm (GA), for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The chaos-like noise and the input/output signals are studied revealing a fractional-order dynamics, characteristic of a long-term system memory.

Robot Trajectory Planning Using Multi-objective Genetic Algorithm Optimization

Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non trivial optimization problem. In this paper a multi-objective genetic algorithm is proposed to address this problem. Multiple criteria are optimized up to five simultaneous objectives. Simulations results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.

Multi-objective maximin sorting scheme

Obtaining a well distributed non-dominated Pareto front is one of the key issues in multi-objective optimization algorithms. This paper proposes a new variant for the elitist selection operator to the NSGA-II algorithm, which promotes well distributed non-dominated fronts. The basic idea is to replace the crowding distance method by a maximin technique. The proposed technique is deployed in well known test functions and compared with the crowding distance method used in the NSGA-II algorithm. This comparison is performed in terms of achieved front solutions distribution by using distance performance indices.

Dynamical modelling of a genetic algorithm

This work addresses the signal propagation and the fractional-order dynamics during the evolution of a genetic algorithm (GA). In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three distinct fitness functions are used to study their influence in the GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.

Optimization of the Workpiece Location in a Machining Robotic Cell

One important issue in a machining robotic cell is the location of the workpiece with respect to the robot. The feasibility of the task, the quality of the final work and the energy consumption, just to mention a few, are all dependent upon it. This can be formulated as an optimization problem where the objective functions are chosen in order to meet desired performance criteria. Typically, the complexity of the problems and the large number of optimization parameters that, usually, are involved, make the genetic algorithms an appropriate tool in this context. In this paper, two optimization problems are formulated: firstly, the power consumed by the manipulator is considered and the problem is solved using a single-objective genetic algorithm; then the stiffness of the manipulator is also included and the respective optimization problem is solved using a multi-objective genetic algorithm. Simulation results are presented for a parallel manipulator robotic cell.

A trajectory planner for manipulators using genetic algorithms

Proposes a genetic algorithm (GA) to generate trajectories for robotic manipulators. The objective is to minimize the ripple in the time evolution of robot positions and velocities. Moreover, the manipulator is required to reach a predefined goal without colliding with obstacles in the workspace. The article presents the results for several redundant and non-redundant robot manipulators.Proposes a genetic algorithm (GA) to generate trajectories for robotic manipulators. The objective is to minimize the ripple in the time evolution of robot positions and velocities. Moreover, the manipulator is required to reach a predefined goal without colliding with obstacles in the workspace. The article presents the results for several redundant and non-redundant robot manipulators.

A GA perspective of the energy requirements for manipulators maneuvering in a workspace with obstacles

This paper proposes a genetic algorithm to generate trajectories for robotic manipulators. The objective is to minimize the ripple in the trajectory time evolution and to minimize the actuator energy requirements without colliding with any obstacles in the workspace. The article presents the results for several redundant and hyper-redundant manipulators.This paper proposes a genetic algorithm to generate trajectories for robotic manipulators. The objective is to minimize the ripple in the trajectory time evolution and to minimize the actuator energy requirements without colliding with any obstacles in the workspace. The article presents the results for several redundant and hyper-redundant manipulators.

Optimization of Parallel Manipulators Using Evolutionary Algorithms

Parallel manipulators have attracted the attention of researchers from different areas such as: high-precision robotics, machine-tools, simulators and haptic devices. The choice of a particular structural configuration and its dimensioning is a central issue to the performance of these manipulators. A solution to the dimensioning problem, normally involves the definition of performance criteria as part of an optimization process. In this paper the kinematic design of a 6-dof parallel robotic manipulator for maximum dexterity is analyzed. The condition number of the inverse kinematic jacobian is defined as the measure of dexterity and solutions that minimize this criterion are found through a genetic algorithm formulation. Subsequently a neuro-genetic formulation is developed and tested. It is shown that the neuro-genetic algorithm can find close to optimal solutions for maximum dexterity, significantly reducing the computational load.

Grey wolf optimization for PID controller design with prescribed robustness margins

The grey wolf optimization algorithm is proposed to design proportional, integrative and derivative controllers using a two degrees of freedom control configuration. The control system is designed in order to achieve good set-point tracking and disturbance rejection performance. The design is accomplished by minimizing an aggregated cost function based on the time-weighted absolute error integral, subjected to robustness constraints. The control system robustness levels are prescribed in terms of the vector margin and maximum complementary sensitivity function values. Simulation results are presented for several common systems dynamics and compared with the ones obtained with a particle swarm optimization algorithm.