Optimization of cooperative motion for humanoid robots using a genetic algorithm

2005 ◽  
Author(s):  
Kook-Jin Choi ◽  
Yoon-Kwon Hwang ◽  
Dae Sun Hong ◽  
Won Jee Chung ◽  
Il-Hwan Park
Keyword(s):  
Genetic Algorithm ◽  
Humanoid Robots ◽  
Cooperative Motion

scholarly journals Structural and Material Optimization for Automatic Synthesis of Spine-Segment Mechanisms for Humanoid Robots with Custom Stiffness Profiles

Materials ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 1982 ◽  
Author(s):  
Adam Ciszkiewicz ◽  
Grzegorz Milewski
Keyword(s):  
Genetic Algorithm ◽  
Humanoid Robots ◽  
Automatic Synthesis ◽  
Artificial Joints ◽  
Flexible Links ◽  
Material Parameters ◽  
Material Optimization ◽  
Automated Method ◽  
Body Joints ◽  
Spine Segment

Typical artificial joints for humanoid robots use actual human body joints only as an inspiration. The load responses of these structures rarely match those of the corresponding joints, which is important when applying the robots in environments tailored to humans. In this study, we proposed a novel, automated method for designing substitutes for a human intervertebral joint. The substitutes were considered as two platforms, connected by a set of flexible links. Their structural and material parameters were obtained through optimization with a structured Genetic Algorithm, based on the reference angular stiffnesses. The proposed approach was tested in three numerical scenarios. In the first test, a mechanism with angular stiffnesses corresponded to the actual L4–L5 intervertebral joint. Scenarios 2 and 3 featured mechanisms with geometry and structure comparable to the joint, but with custom stiffness profiles. The obtained results proved the effectiveness of the proposed method. It could be employed in the design of artificial joints for humanoid robots and orthotic structures for the human spine. As the approach is general, it could also be extended to different body joints.

scholarly journals Humanoid Robot Cooperative Motion Control Based on Optimal Parameterization

2021 ◽  
Vol 15 ◽  
Author(s):  
Qiubo Zhong ◽  
Yaoyun Li ◽  
Caiming Zheng ◽  
Tianyao Shen
Keyword(s):  
Energy Consumption ◽  
Humanoid Robot ◽  
Control Method ◽  
Humanoid Robots ◽  
Parameters Optimization ◽  
Optimal Parameters ◽  
Cooperative Motion ◽  
Key Technologies ◽  
Consumption Index ◽  
The Stability

The implementation of low-energy cooperative movements is one of the key technologies for the complex control of the movements of humanoid robots. A control method based on optimal parameters is adopted to optimize the energy consumption of the cooperative movements of two humanoid robots. A dynamic model that satisfies the cooperative movements is established, and the motion trajectory of two humanoid robots in the process of cooperative manipulation of objects is planned. By adopting the control method with optimal parameters, the parameters optimization of the energy consumption index function is performed and the stability judgment index of the robot in the movement process is satisfied. Finally, the effectiveness of the method is verified by simulations and experimentations.

scholarly journals State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm

10.5772/50918 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 23 ◽  
Author(s):  
Xuyang Wang ◽  
Tiansheng Lu ◽  
Peiyan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Humanoid Robots ◽  
The State ◽  
Stable States ◽  
New Approach ◽  
Motion Data ◽  
Reference Motion ◽  
State Generation ◽  
Original Motion

A new approach to generate the original motion data for humanoid motion planning is presented in this paper. And a state generator is developed based on the genetic algorithm, which enables users to generate various motion states without using any reference motion data. By specifying various types of constraints such as configuration constraints and contact constraints, the state generator can generate stable states that satisfy the constraint conditions for humanoid robots. To deal with the multiple constraints and inverse kinematics, the state generation is finally simplified as a problem of optimizing and searching. In our method, we introduce a convenient mathematic representation for the constraints involved in the state generator, and solve the optimization problem with the genetic algorithm to acquire a desired state. To demonstrate the effectiveness and advantage of the method, a number of motion states are generated according to the requirements of the motion.

Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Asita Kumar Rath ◽  
Dayal R. Parhi ◽  
Harish Chandra Das ◽  
Priyadarshi Biplab Kumar ◽  
Manjeet Kumar Mahto
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Turning Angle ◽  
Content Type ◽  
Cluttered Environments ◽  
Hybrid Controller ◽  
Neural Network Controller ◽  
Network Controller

PurposeTo navigate humanoid robots in complex arenas, a significant level of intelligence is required which needs proper integration of computational intelligence with the robot's controller. This paper describes the use of a combination of genetic algorithm and neural network for navigational control of a humanoid robot in given cluttered environments.Design/methodology/approachThe experimental work involved in the current study has been done by a NAO humanoid robot in laboratory conditions and simulation work has been done by the help of V-REP software. Here, a genetic algorithm controller is first used to generate an initial turning angle for the robot and then the genetic algorithm controller is hybridized with a neural network controller to generate the final turning angle.FindingsFrom the simulation and experimental results, satisfactory agreements have been observed in terms of navigational parameters with minimal error limits that justify the proper working of the proposed hybrid controller.Originality/valueWith a lack of sufficient literature on humanoid navigation, the proposed hybrid controller is supposed to act as a guiding way towards the design and development of more robust controllers in the near future.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy
Sign in / Sign up

Export Citation Format

Share Document