Path planning of a Robot Manipulator using Retrieval RRT Strategy
Kyongsae Oh, Euntai Kim, Young-Wan Cho, International Journal of Fuzzy Logic and Intelligent Systems, 2007, Volume 7, Issue 2, pp 138-142.
Abstract
This paper presents an algorithm which extends the rapidly-exploring random tree (RRT) framework to deal with change of the task environments. This algorithm called the Retrieval RRT Strategy (RRS) combines a support vector machine (SVM) and RRT and plans the robot motion in the presence of the change of the surrounding environment. This algorithm consists of two levels. At the first level, the SVM is built and selects a proper path from the bank of RRTs for a given environment. At the second level, a real path is planned by the RRT planners for the given environment. The suggested method is applied to the control of KUKA™, a commercial 6 DOF robot manipulator, and its feasibility and efficiency are demonstrated via the cosimulatation of MatLab™ and RecurDyn™.
How Multibody Dynamics Simulation Technology is Used
Through co-simulation of RecurDyn and Simulink robot motion can be intelligently planned in a changing environment. RecurDyn can be used to change the environment around the robot to ensure the control algorithm is robust for a large number of environments.
Get This Paper
Related Case Studies
- Development of High Fidelity Mobility Simulation of an Autonomous Vehicle in an Off-Road Scenario Using Integrated Sensor, Controller, and Multi-Body Dynamics
- Sliding mode control of locomotion for a biomimetic robot inspired by pillbugs
- Multidisciplinary parametric design and evaluation of six degrees of freedom mechanical arm
- Parameter study on the grasping characteristics of the humanoid robot hand with spherical four bar linkages
