In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. \(\breve\)lajpah, L.: On orientation control of functional redundant robots. Zhang, Y., Li, J., Zhang, Z.: A time-varying coefficient-based manipulability-maximizing scheme for motion control of redundant robots subject to varying joint-velocity limits. Rodríguez, H., Banfield, I.: Inverse kinematic multiobjective optimization for a vehicle-arm robot system using evolutionary algorithms. In: 2020 6th International Conference on Control, Automation and Robotics (ICCAR), pp. Xu, P., Yao, X., Chen, L., Liu, K., Bi, G.: Heuristic kinematics of a redundant robot-positioner system for additive manufacturing. Zanchettin, A.M., Rocco, P.: On the use of functional redundancy in industrial robotic manipulators for optimal spray painting. In: 2011 3rd International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. Lotz, M., Bruhm, H., Czinki, A., Zalewski, M.: A real-time motion control strategy for redundant robots improving dynamics and accuracy. Subrin, K., Sabourin, L., Cousturier, R., Gogu, G., Mezouar, Y.: New redundant architectures in machining: serial and parallel robots. Gonul, B., Faruk Sapmaz, O., Taner Tunc, L.: Improved stable conditions in robotic milling by kinematic redundancy. Jiao, J., Tian, W., Liao, W., Zhang, L., Yin, B.: Processing configuration off-line optimization for functionally redundant robotic drilling tasks. Shahabi, M., Ghariblu, H.: Optimal joint motion for complicated welding geometry by a redundant robotic system. (ed.) Advances in Mechanism and Machine Science. Schappler, M., Tappe, S., Ortmaier, T.: Resolution of functional redundancy for 3T2R robot tasks using two sets of reciprocal Euler angles. Huo, L.: Robotic joint motion optimization of functionally redundant task for joint limits and singularity avoidance. īorboni, A., Bussola, R., Faglia, R., Magnani, P.L., Menegolo, A.: Movement optimization of a redundant serial robot for high-quality pipe cutting. Stavropoulos, P., Foteinopoulos, P., Papacharalampopoulos, A., Bikas, H.: Addressing the challenges for the industrial application of additive manufacturing: towards a hybrid solution. Multi-Objective Genetic Algorithms, MOGA, is used to solve the Inverse Kinematics (IK) for global optimization of functionally redundant robots, for incomplete orientation constrained task. To overcome this difficulty, in this work, the Pareto Front is used to determine the topology of the selfmotions generated by redundancy and from these to identify the invertible subregions between the operational space and the configuration space. Due to this characteristic, during the optimization process by means of the Pareto Front technique, a sufficiently diverse set of Pareto Optimum points is not obtained to ensure that a solution close to the best combination of the multiple objectives can be found for a global optimization of the robot path. Moreover, each configuration lies on a specific solution branch into joint space. This is the case for the most commonly used non-redundant 6 DoF serial robot manipulators. For the case of functional redundancy, where the redundant parameters are within the operational space, once the optimal parameters have been selected, the inverse mapping between the operational space and the joint space often corresponds to a closed solution, which means that there are a finite number of joints configurations. Recently, industrial serial robots of 6 DOF are being widely used for a variety of tasks that only require 5 DOF or less, which is causes functional redundancy. The kinematic redundancy of the robot with respect to specific task can be exploited to optimize a desired criterion. Off-line analysis of path tracking of functionally redundant robot manipulators is essential for effective use of industrial robots.
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