Abstract

Power consumption and accuracy are main aspects to be taken into account in the movement executed by high performance robots. The first aspect is important from the economical point of view, while the second is requested to satisfy technical specifications. Aiming at increasing the robot performance, a strategy that maximizes the manipulator accuracy and minimizes the mechanical power consumption is considered in this work. The end-effector is constrained to follow a predefined path during the optimal task positioning. The proposed strategy defines a relation between mechanical power and manipulability as a key element of the manipulator analysis, establishing a performance index for a rigid body transformation. This transformation is used to compute the optimal task positioning through the optimization of a multicriteria objective function. Numerical simulations regarding a serial robot manipulator demonstrate the viability of the proposed methodology.

Highlights

  • Minimization of production costs and maximization of productivity are some of the major objectives of industrial automation

  • The consideration of the dynamics behavior of a serial robot manipulator is of great importance for its path planning

  • An approach to increase the manipulability while decreasing dynamics requirements was proposed by using optimization techniques

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Summary

Introduction

Minimization of production costs and maximization of productivity are some of the major objectives of industrial automation. With the aim of increasing productivity in the path following, industrial robotic applications have been addressed in the literature by determining path-constrained time-optimal motions, by taking into account the torque limits of the actuators. In these formulations, the joint actuator torques are considered as controlled inputs and the open loop control schemes result in bang-bang or bang-singular-bang controls (Bobrow [7], Chen [19], Shiller [20]).

Manipulability
Mechanical Power
Task Specification
Multicriteria Programming
The Proposed Formulation
Optimization Strategy
Numerical Simulations
Second Experiment
Third Experiment
Initial 6
Conclusions
Findings
10. References
Full Text
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