Pareto optimality and multiobjective trajectory planning for a 7-DOF redundant manipulator

Authors:
Alexis Guigue;Mojtaba Ahmadi;Rob Langlois;M. John D. Hayes
Affiliations:
Department of Aerospace and Mechanical Engineering, Carleton University, Ottawa, ON, Canada;Department of Aerospace and Mechanical Engineering, Carleton University, Ottawa, ON, Canada;Department of Aerospace and Mechanical Engineering, Carleton University, Ottawa, ON, Canada;Department of Aerospace and Mechanical Engineering, Carleton University, Ottawa, ON, Canada
Venue:
IEEE Transactions on Robotics
Year:
2010

Citing 2
Cited 0

Approximation algorithms for NP-hard problems

Approximation algorithms for NP-hard problems
A Discrete Dynamic Programming Approximation to the Multiobjective Deterministic Finite Horizon Optimal Control Problem

SIAM Journal on Control and Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel approach to solve multiobjective robotic trajectory planning problems. It proposes to find the Pareto optimal set, rather than a single solution usually obtained through scalarization, e.g., weighting the objective functions. Using the trajectory planning problem for a redundant manipulator as part of a captive trajectory simulation system, the general discrete dynamic programming (DDP) approximation method presented in our previous work is shown to be a promising approach to obtain a close representation of the Pareto optimal set. When compared with the set obtained by varying the weights, the results confirm that the DDP approximation method can find approximate Pareto objective vectors, where the weighting method fails, and can generally provide a closer representation of the actual Pareto optimal set.