On Motion Planning and Control for Partially Differentially Flat Systems

Robotica ◽  
2020 ◽  
pp. 1-17
Author(s):  
Yang Bai ◽  
Mikhail Svinin ◽  
Evgeni Magid ◽  
Yujie Wang
Keyword(s):  
Motion Planning ◽  
Beta Function ◽  
Computational Cost ◽  
Adaptive Controller ◽  
Approximation Technique ◽  
Similar System ◽  
Planning And Control ◽  
Flat Systems ◽  
Differentially Flat Systems ◽  
And Control

SUMMARY This paper deals with motion planning and control problems for a class of partially differentially flat systems. They possess a feature that the derivative of the fiber variable can be represented purely by the base variable and its derivatives. Based on this feature, a Beta function-based motion planning algorithm is proposed with less computational cost compared with the optimal control formulation while providing similar system performance. Then, an adaptive controller is constructed through a function approximation technique-based approach. Finally, the feasibility of the proposed motion planning and control algorithms is verified by simulations.

scholarly journals Human-Following Motion Planning and Control Scheme for Collaborative Robots Based on Human Motion Prediction

2021 ◽  
Author(s):  
Fahad Iqbal Khawaja ◽  
Akira Kanazawa ◽  
Jun Kinugawa ◽  
Kazuhiro Kosuge
Keyword(s):  
Motion Planning ◽  
Human Motion ◽  
Human Robot Interaction ◽  
Collaborative Robots ◽  
Sensing System ◽  
Planning And Control ◽  
Control Scheme ◽  
Active Research ◽  
And Control ◽  
Collaborative Robot

Human-Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist the human workers in their tasks and improve their efficiency. But the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC) based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker.

Sign in / Sign up

Export Citation Format

Share Document