In this paper, we present a novel nonlinear analytical coupled trajectory optimization of a 7-DOF Baxter manipulator validated through experimental work utilizing global optimization tools. The robotic manipulators used in network-based applications of industrial units and even homes, for disabled patients, spend significant lumped amount of energy and therefore, optimal trajectories need to be generated to address efficiency issues. We here examine both heuristic (Genetics) and gradient based (GlobalSearch) algorithms for a novel approach of “S-Shaped” trajectory (unlike conventional polynomials), to avoid being trapped in several possible local minima along with yielding minimal computational cost, enforcing operational time and torque saturation constraints. The global schemes are utilized in minimizing the lumped amount of energy consumed in a nominal path given in the collision-free joint space except an impact between the robot’s end effector and a target object for the nominal operation. Note that such robots are typically operated for thousands of cycles resulting in a considerable cost of operation. Due to the expected computational cost of such global optimization algorithms, step size analysis is carried out to minimize both the computational cost (iteration) and possibly cost function by finding an optimal step size. Global design sensitivity analysis is also performed to examine the effects of changes of optimization variables on the cost function defined.
- Dynamic Systems and Control Division
Experimental and Novel Analytical Trajectory Optimization of a 7-DOF Baxter Robot: Global Design Sensitivity and Step Size Analyses
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Bagheri, M, Naseradinmousavi, P, & Morsi, R. "Experimental and Novel Analytical Trajectory Optimization of a 7-DOF Baxter Robot: Global Design Sensitivity and Step Size Analyses." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems. Tysons, Virginia, USA. October 11–13, 2017. V001T30A001. ASME. https://doi.org/10.1115/DSCC2017-5004
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