Motion planning for a redundant manipulator by genetic algorithm using an evaluation function extracted from skilled operators

2002 ◽  
Author(s):  
T. Shibata ◽  
T. Abe ◽  
K. Tanie ◽  
M. Nose
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Evaluation Function ◽  
Redundant Manipulator

A new motion planning method for discretely actuated hyper-redundant manipulators

Robotica ◽  
2015 ◽  
Vol 35 (1) ◽  
pp. 101-118 ◽  
Author(s):  
Alireza Motahari ◽  
Hassan Zohoor ◽  
Moharam Habibnejad Korayem
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Trajectory Tracking ◽  
Inverse Kinematic ◽  
Planning Problem ◽  
Redundant Manipulators ◽  
Tracking Problem ◽  
Parallel Mechanisms ◽  
Redundant Manipulator ◽  
The Common

SUMMARYA hyper-redundant manipulator is made by mounting the serial and/or parallel mechanisms on top of each other as modules. In discrete actuation, the actuation amounts are a limited number of certain values. It is not feasible to solve the kinematic analysis problems of discretely actuated hyper-redundant manipulators (DAHMs) by using the common methods, which are used for continuous actuated manipulators. In this paper, a new method is proposed to solve the trajectory tracking problem in a static prescribed obstacle field. To date, this problem has not been considered in the literature. Theremoving first collision(RFC) method, which is originally proposed for solving the inverse kinematic problems in the obstacle fields was modified and used to solve the motion planning problem. For verification, the numerical results of the proposed method were compared with the results of thegenetic algorithm(GA) method. Furthermore, a novel DAHM designed and implemented by the authors is introduced.

Motion Planning for a Redundant Manipulator Using Genetic Algorithm

2011 ◽  
Vol 467-469 ◽  
pp. 782-787 ◽  
Author(s):  
S. Parasuraman ◽  
Chiew Mun Hou ◽  
V. Ganapathy
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Planning ◽  
Collision Detection ◽  
Angular Displacement ◽  
Joint Space ◽  
Redundant Manipulators ◽  
Evaluation Function ◽  
Redundant Manipulator ◽  
End Effector ◽  
Total Displacement

The trajectory planning of redundant manipulator is key areas of research, which require efficient optimization algorithms. This paper presents a new method that combines multiple objectives for trajectory planning and generation for redundant manipulators. The algorithm combines collision detection, finding target and optimizing trajectory using Genetic algorithm. In order to optimize the path, an evaluation function is defined based on multiple criteria, including the total displacement of the end-effector, the total angular displacement of all the joints, as well as the uniformity of Cartesian and joint space velocities. These criteria result in minimized, smooth end-effector motions. These algorithm yields solutions instantaneously and generate the path. The proposed algorithm is analyzed and its performance is demonstrated through simulation and the results are compared with the other methods.

Motion planning by genetic algorithm for a redundant manipulator using a model of criteria of skilled operators

1997 ◽  
Vol 102 (1-4) ◽  
pp. 171-186 ◽  
Author(s):  
Takanori Shibata ◽  
Tamotsu Abe ◽  
Kazuo Tanie ◽  
Matsuo Nose
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Redundant Manipulator

Motion Planning of a Nonholonomic Multibody System Using Genetic Algorithm

2003 ◽  
Author(s):  
Xin-Sheng Ge ◽  
Li-Qun Chen
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Nonholonomic System ◽  
Multibody System ◽  
Planning Problem ◽  
Control Approach ◽  
Velocity Constraints ◽  
Nonholonomic Motion Planning ◽  
Optimal Control Approach ◽  
Motion Planning Problem

The motion planning problem of a nonholonomic multibody system is investigated. Nonholonomicity arises in many mechanical systems subject to nonintegrable velocity constraints or nonintegrable conservation laws. When the total angular momentum is zero, the control problem of system can be converted to the motion planning problem for a driftless control system. In this paper, we propose an optimal control approach for nonholonomic motion planning. The genetic algorithm is used to optimize the performance of motion planning to connect the initial and final configurations and to generate a feasible trajectory for a nonholonomic system. The feasible trajectory and its control inputs are searched through a genetic algorithm. The effectiveness of the genetic algorithm is demonstrated by numerical simulation.

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