Every-Efficient Motion Planning for Dual-Armed Robot by PID Gain Optimization with Genetic Algorithm

2021 ◽  
Author(s):  
Kazuki Nonoyama ◽  
Tatsushi Nishi
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Gain Optimization

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.

Optimized inchworm motion planning for a novel in-pipe robot

2013 ◽  
Vol 228 (7) ◽  
pp. 1248-1258 ◽  
Author(s):  
Qingyou Liu ◽  
Yonghua Chen ◽  
Tao Ren ◽  
Ying Wei
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Strain Energy ◽  
Modern Society ◽  
Robot Design ◽  
Stroke Length ◽  
Velocity Pattern ◽  
Optimization Result ◽  
Gradient Based ◽  
Inspection And Maintenance

Modern society is fueled by very comprehensive grids of gas and liquid supply pipelines. The frequent inspection and maintenance of such pipeline grids is not a trivial task. It has been demonstrated that such task is best performed by using in-pipe robots. In this paper, a novel inchworm robot design and its optimized motion planning are presented. The proposed design uses a helical drive for both gripping and locomotion of the robot. The extension and retraction between inchworm segments are facilitated by conic springs as they can store strain energy. The proposed inchworm robot can also be made very compact without sacrificing stroke length as conic springs can be easily designed with telescopic feature. To generate inchworm motion, a sinusoidal velocity pattern is planned for each segment. The frequency of the velocity pattern is optimized using a genetic algorithm (GA). The optimization result from the GA method has been validated using a traditional gradient based method.

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