The article considers the problem of planning the optimal trajectory of a delta robot. The workspace of the robot is limited by the range of permissible values of the angles of the drive revolute joints, interference of links and singularities. Additional constraints related to the presence of obstacles have been introduced. Acceptable values of the robot’s input coordinates are obtained based on the inverse kinematics, taking into account the constraints of the workspace, represented as a partially ordered set of integers. For the given initial and final coordinates, a randomly generated family of trajectories belonging to a valid set is obtained. Optimization of each of the trajectories of the family based on evolutionary algorithms is performed. The optimization criterion is a function proportional to the duration of movement along the trajectory. The results of modeling are presented.

Synthesis of Trajectory Planning Algorithms Using Evolutionary Optimization Algorithms

Malyshev D.
;
2022-01-01

Abstract

The article considers the problem of planning the optimal trajectory of a delta robot. The workspace of the robot is limited by the range of permissible values of the angles of the drive revolute joints, interference of links and singularities. Additional constraints related to the presence of obstacles have been introduced. Acceptable values of the robot’s input coordinates are obtained based on the inverse kinematics, taking into account the constraints of the workspace, represented as a partially ordered set of integers. For the given initial and final coordinates, a randomly generated family of trajectories belonging to a valid set is obtained. Optimization of each of the trajectories of the family based on evolutionary algorithms is performed. The optimization criterion is a function proportional to the duration of movement along the trajectory. The results of modeling are presented.
2022
978-3-031-22989-3
978-3-031-22990-9
Genetic algorithm
Grey Wolf optimization
Particle swarm optimization
Trajectory planning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/362820
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