Multi-objective trajectory planning of humanoid robot using hybrid controller for multi-target problem in complex terrain

Kashyap, Abhishek; Parhi, Dayal R.

doi:10.1016/j.eswa.2021.115110

Cited by 7 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on Niched Genetic Algorithms (NGA) and fitness sharing technology, a dual threaded method utilizing elite strategy characteristics is designed. Kashyap and Parhi [5] emphasized the development and implementation of a hybrid navigation controller to optimize path length, energy requirements, and the time required to complete specified tasks. The navigation controller is implemented through a hybrid meta heuristic improved Spider Monkey Optimization (ISMO) method and Regression Analysis (RA).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Trajectory Planning Application of Rehabilitation Robots Based on Improved CSA Algorithm

Xia

2023

IEEE Access

View full text Add to dashboard Cite

With the aging global population, there are more patients with hemiplegia caused by stroke. The demand for rehabilitation robots to assist rehabilitation therapists is also increasing. To solve the above problems, a reasonable trajectory planning of the rehabilitation robot joints is constructed to achieve rehabilitation assistance, improve safety and practicality.Firstly, a Kinematics model is constructed, and B-spline is applied to realize the trajectory planning of the rehabilitation robot. By introducing crow search algorithm and multiple crow search algorithms, the energy impact multi-objective optimization for the upper limb movement of the rehabilitation robot is carried out. Relevant experiments are designed to compare and verify the optimization effects of the algorithms. From the results, by optimizing the crow search algorithm, only 25.36% and 34.20% of the impact and energy are used. The average impact size per unit of time decreases by 74.68%. The energy consumption per unit time decreases by 65.39%.After optimizing the crow search algorithm, the impact and energy per unit time are only 14.41% and 17.25%, respectively. The energy per unit time decreases by 74.53%, and the impact decreases by 82.97%. Compared with other algorithms, the space complexity of the improved method is very small, and the standard deviation is 0.03. The optimization algorithm performs well in trajectory planning optimization. It is of great significance in the trajectory planning and rehabilitation role of rehabilitation robots.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Then the equation is derived to obtain the driving torque of each joint [17], [18], [19]. The relevant expression is shown in Equation (5).…”

Section: A Kinematics and Tp Of Rehabilitation Robotmentioning

confidence: 99%

Trajectory Planning Application of Rehabilitation Robots Based on Improved CSA Algorithm

Xia

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…According to differences in the planning space, trajectory planning can be divided into Cartesian space trajectory planning and joint space trajectory planning. They must both meet the kinematic and dynamic constraints of the robot manipulator, and the trajectory must be continuous, smooth, and impact-free within the performance requirements of the robot manipulator's components; that is, the speed and acceleration must not have sudden changes [10]. At present, the research on optimal trajectory planning mainly focuses on time-optimal trajectory planning, energy-optimal trajectory planning, impact-optimal trajectory planning, and hybrid optimal trajectory planning [11][12][13].…”

Section: Related Workmentioning

confidence: 99%

“…where λ e0 is the bounded set of W e , λ e is the bounded set of x e , and W e is updated online by Equation (10) to guarantee an acceptable estimation of W e .…”

Section: Dynamics Analysismentioning

confidence: 99%

See 1 more Smart Citation

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Song

Bai

et al. 2021

Entropy

View full text Add to dashboard Cite

Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

show abstract

Multi-objective time-energy-impact optimization for robotic excavator trajectory planning

Feng,

Jiang,

Ding

et al. 2023

Automation in Construction

View full text Add to dashboard Cite

Multi-objective trajectory planning of humanoid robot using hybrid controller for multi-target problem in complex terrain

Cited by 7 publications

References 48 publications

Trajectory Planning Application of Rehabilitation Robots Based on Improved CSA Algorithm

Trajectory Planning Application of Rehabilitation Robots Based on Improved CSA Algorithm

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Multi-objective time-energy-impact optimization for robotic excavator trajectory planning

Contact Info

Product

Resources

About