TY - GEN
T1 - Multi-objective GA for Collision Avoidance on Robot Manipulators Based on Artificial Potential Field
AU - Cea-Montufar, César E.
AU - Merchán-Cruz, Emmanuel A.
AU - Ramírez-Gordillo, Javier
AU - Gutiérrez-Mejía, Bárbara M.
AU - Vergara-Hernández, Erasto
AU - Nava-Vega, Adriana
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - This paper presents a path planning strategy for robotic manipulators based on genetic algorithms, dual quaternions and artificial potential field, designing a multi-objective function that allow trajectories be planned avoiding collisions in the workspace and singularity-free kinematic restrictions for manipulators as an optimization problem, satisfying position and orientation conditions. Its analysis is based on the problem of generating a trajectory followed by a sequence of coordinated movements capable of moving the manipulator to perform tasks in the workspace, the problem is not only generated these movements, but also implement strategies that define the path with tools that are easy to implement and avoid obstacles autonomously. Robot kinematics solved by dual quaternion can be used to combine translation with orientation on robotic manipulators in a systematic way, simplifying calculation operations compatible with conventional methods. The artificial potential field approach has been extended to collision avoidance for all manipulator links. A genetic algorithm is used to solve the problem, which the fitness of the problem can be measured by a multi-objective function that involves the distance between the initial and desired position/orientation, minimum joint displacement, dual quaternion configuration, the use of attraction potential to the goal and a repulsion potential to the obstacles and its own links.
AB - This paper presents a path planning strategy for robotic manipulators based on genetic algorithms, dual quaternions and artificial potential field, designing a multi-objective function that allow trajectories be planned avoiding collisions in the workspace and singularity-free kinematic restrictions for manipulators as an optimization problem, satisfying position and orientation conditions. Its analysis is based on the problem of generating a trajectory followed by a sequence of coordinated movements capable of moving the manipulator to perform tasks in the workspace, the problem is not only generated these movements, but also implement strategies that define the path with tools that are easy to implement and avoid obstacles autonomously. Robot kinematics solved by dual quaternion can be used to combine translation with orientation on robotic manipulators in a systematic way, simplifying calculation operations compatible with conventional methods. The artificial potential field approach has been extended to collision avoidance for all manipulator links. A genetic algorithm is used to solve the problem, which the fitness of the problem can be measured by a multi-objective function that involves the distance between the initial and desired position/orientation, minimum joint displacement, dual quaternion configuration, the use of attraction potential to the goal and a repulsion potential to the obstacles and its own links.
KW - Artificial potential field
KW - Dual quaternion
KW - Multi-objective genetic algorithm
KW - Path planning
KW - Robot manipulator
UR - http://www.scopus.com/inward/record.url?scp=85075681735&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33749-0_55
DO - 10.1007/978-3-030-33749-0_55
M3 - Contribución a la conferencia
AN - SCOPUS:85075681735
SN - 9783030337483
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 687
EP - 700
BT - Advances in Soft Computing - 18th Mexican International Conference on Artificial Intelligence, MICAI 2019, Proceedings
A2 - Martínez-Villaseñor, Lourdes
A2 - Batyrshin, Ildar
A2 - Marín-Hernández, Antonio
PB - Springer
T2 - 18th Mexican International Conference on Artificial Intelligence, MICAI 2019
Y2 - 27 October 2019 through 2 November 2019
ER -