Multiobjective trajectory planner for industrial robots with payload constraints

Authors:
R. Saravanan;S. Ramabalan;C. Balamurugan
Affiliations:
Department of mechatronics engineering, kumaraguru college of technology, coimbatore – pin: 641 006, tamil nadu;Faculty of cad/cam (p.g. course), j. j. college of engineering and technology, tiruchirapalli – pin: 620 009;Faculty of cad/cam (p.g. course), j. j. college of engineering and technology, tiruchirapalli – pin: 620 009
Venue:
Robotica
Year:
2008

Citing 3
Cited 1

The NURBS book (2nd ed.)

The NURBS book (2nd ed.)
An optimum robot path planning with payload constraints

Robotica
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Optimal balancing of robot manipulators in point-to-point motion

Robotica

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Abstract

A general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of the NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MODE are best for this problem.