Fares J. Abu-Dakka

Adaptation of manipulation skills in physical contact with the environment to reference force profiles

We propose a new methodology for learning and adaption of manipulation skills that involve physical contact with the environment. Pure position control is unsuitable for such tasks because even small errors in the desired trajectory can cause significant deviations from the desired forces and torques. The proposed algorithm takes a reference Cartesian trajectory and force/torque profile as input and adapts the movement so that the resulting forces and torques match the reference profiles. The learning algorithm is based on dynamic movement primitives and quaternion representation of orientation, which provide a mathematical machinery for efficient and stable adaptation. Experimentally we show that the robot’s performance can be significantly improved within a few iteration steps, compensating for vision and other errors that might arise during the execution of the task. We also show that our methodology is suitable both for robots with admittance and for robots with impedance control.

Solving peg-in-hole tasks by human demonstration and exception strategies

Purpose – The purpose of this paper is to propose a new algorithm based on programming by demonstration and exception strategies to solve assembly tasks such as peg-in-hole. Design/methodology/approach – Data describing the demonstrated tasks are obtained by kinesthetic guiding. The demonstrated trajectories are transferred to new robot workspaces using three-dimensional (3D) vision. Noise introduced by vision when transferring the task to a new configuration could cause the execution to fail, but such problems are resolved through exception strategies. Findings – This paper demonstrated that the proposed approach combined with exception strategies outperforms traditional approaches for robot-based assembly. Experimental evaluation was carried out on Cranfield Benchmark, which constitutes a standardized assembly task in robotics. This paper also performed statistical evaluation based on experiments carried out on two different robotic platforms. Practical implications – The developed framework can have an...

Transfer of assembly operations to new workpiece poses by adaptation to the desired force profile

In this paper we propose a new algorithm that can be used for adaptation of robot trajectories in automated assembly tasks. Initial trajectories and forces are obtained by demonstration and iteratively adapted to specific environment configurations. The algorithm adapts Cartesian space trajectories to match the forces recorded during the human demonstration. Experimentally we show the effectiveness of our approach on learning of Peg-in-Hole (PiH) task. We performed our experiments on two different robotic platforms with workpieces of different shapes.

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

This paper presents a new genetic algorithm methodology to solve the trajectory planning problem. This methodology can obtain smooth trajectories for industrial robots in complex environments using a direct method. The algorithm simultaneously creates a collision-free trajectory between initial and final configurations as the robot moves. The presented method deals with the uncertainties associated with the unknown kinematic properties of intermediate via points since they are generated as the algorithm evolves looking for the solution. Additionally, the objective of this algorithm is to minimize the trajectory time, which guides the robot motion. The method has been applied successfully to the PUMA 560 robotic system. Four operational parameters (execution time, computational time, end-effector distance travelled and significant points distance travelled) have been computed to study and analyze the algorithm efficiency. The experimental results show that, the proposed optimization algorithm for the trajectory planning problem of an industrial robot is feasible.

Comparing the efficiency of five algorithms applied to path planning for industrial robots

Purpose – The purpose of this paper is to compare the quality and efficiency of five methods for solving the path planning problem of industrial robots in complex environments.Design/methodology/approach – In total, five methods are presented for solving the path planning problem and certain working parameters have been monitored using each method. These working parameters are the distance travelled by the robot and the computational time needed to find a solution. A comparison of results has been analyzed.Findings – After this study, it could be easy to know which of the proposed methods is most suitable for application in each case, depending on the parameter the user wants to optimize. The findings have been summarized in the conclusion section.Research limitations/implications – The five techniques which have been developed yield good results in general.Practical implications – The algorithms introduced are able to solve the path planning problem for any industrial robot working with obstacles.Social ...

Evolutionary Path Planning Algorithm for Industrial Robots

Abstract This paper proposed a new methodology to solve collision free path planning problem for industrial robot using genetic algorithms. The method poses an optimization problem that aims to minimize the significant points traveling distance of the robot. The behavior of more two operational parameters – the end effector traveling distance and computational time – are analyzed. This algorithm is able to obtain the solution for any industrial robot working in the complex environments, just it needs to choose a suitable significant points for that robot. An application example has been illustrated using robot Puma 560.

Parallel-Populations genetic algorithm for the optimization of cubic polynomial joint trajectories for industrial robots

In this paper a parallel-populations genetic algorithm procedure is presented for the obtainment of minimum-time trajectories for industrial robots. This algorithm is fed in first place by a sequence of configurations then cubic spline functions are used for the construction of joint trajectories for industrial robots. The algorithm is subjected to two types of constraints: (1) Physical constraints on joint velocities, accelerations, and jerk. (2) Dynamic constraints on torque, power, and energy. Comparison examples are used to evaluate the method with different combinations of crossover and mutation.