Trajectory Generation of an Industrial Robot With Constrained Kinematic and Dynamic Variations for Improving Positional Accuracy

1. Introduction

Generally higher repeatability and accuracy are need to be achieved for the robotic manipulators typically used for medical and industrial applications (Rout et al., 2019). In industrial applications the robots are programmed with the end effector positions in real world co-ordinate system in advance and then robots are travelled along the defined trajectory path for performing the task in repetitive manner. A number of intermittent points are first defined on trajectory path and then inverse kinematics has been implemented to obtain the robot arm joint angular displacements for the corresponding defined points. This is known as off-line trajectory planning (Gasparetto & Zanotto, 2010; Sciavicco & Siciliano, 2012). For the off-line trajectory planning of industrial manipulators, the end effector orientation and positional errors are need to be kept in specified tolerance and limits over cycles so that a cost effective way for effective utilization of industrial robots can be achieved. The repeatability and accuracy of robot end-effector positioning can be achieved by analyzing the uncertainties which influence the performance of manipulator. Out of these uncertainties the kinematics and dynamics of manipulator play a major role and can be analyzed by trajectory planning of robot joints so that positional accuracy and less actuator effect with higher productivity can be achieved.

For the cost effective incorporation of robots in industrial applications, the robot travel for the defined tasks should be completed within minimum possible time. Initially to achieve higher productivity the primary objective of the trajectory planning has been based on minimization of total travel time. Schoenwald et al. (Schoenwald et al., 1991) developed a finite element model for computation of actuator dynamics which are considered as the constraints for minimization of time for trajectory generation. Dakka et al. (Abu-Dakka et al., 2017) presented the trajectory planning algorithm using Genetic Algorithm for minimizing total robot travel time which is subjected to kinematic, dynamic and payload constraints.