Regionally Growing Random Trees: A synergistic motion planning and control algorithm for dynamic systems

2016 ◽  
Author(s):  
Siamak G. Faal ◽  
Cagdas D. Onal
Keyword(s):  
Motion Planning ◽  
Dynamic Systems ◽  
Control Algorithm ◽  
Random Trees ◽  
Planning And Control ◽  
And Control

scholarly journals How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4012
Author(s):  
Milad Karimshoushtari ◽  
Carlo Novara ◽  
Fabio Tango
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Urban Areas ◽  
Control Algorithm ◽  
Transportation Systems ◽  
Imitation Learning ◽  
Planning And Control ◽  
Computation Efficiency ◽  
And Control

Interest in autonomous vehicles (AVs) has significantly increased in recent years, but despite the huge research efforts carried out in the field of intelligent transportation systems (ITSs), several technological challenges must still be addressed before AVs can be extensively deployed in any environment. In this context, one of the key technological enablers is represented by the motion-planning and control system, with the aim of guaranteeing the occupants comfort and safety. In this paper, a trajectory-planning and control algorithm is developed based on a Model Predictive Control (MPC) approach that is able to work in different road scenarios (such as urban areas and motorways). This MPC is designed considering imitation-learning from a specific dataset (from real-world overtaking maneuver data), with the aim of getting human-like behavior. The algorithm is used to generate optimal trajectories and control the vehicle dynamics. Simulations and Hardware-In-the-Loop tests are carried out to demonstrate the effectiveness and computation efficiency of the proposed approach.

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