Abstract
In a future intelligent factory, a robotic manipulator must work efficiently and safely in a Human–Robot collaborative and dynamic unstructured environment. Autonomous path planning is the most important issue which must be resolved first in the process of improving robotic manipulator intelligence. Among the path-planning methods, the Rapidly Exploring Random Tree (RRT) algorithm based on random sampling has been widely applied in dynamic path planning for a high-dimensional robotic manipulator, especially in a complex environment because of its probability completeness, perfect expansion, and fast exploring speed over other planning methods. However, the existing RRT algorithm has a limitation in path planning for a robotic manipulator in a dynamic unstructured environment. Therefore, an autonomous obstacle avoidance dynamic path-planning method for a robotic manipulator based on an improved RRT algorithm, called Smoothly RRT (S-RRT), is proposed. This method that targets a directional node extends and can increase the sampling speed and efficiency of RRT dramatically. A path optimization strategy based on the maximum curvature constraint is presented to generate a smooth and curved continuous executable path for a robotic manipulator. Finally, the correctness, effectiveness, and practicability of the proposed method are demonstrated and validated via a MATLAB static simulation and a Robot Operating System (ROS) dynamic simulation environment as well as a real autonomous obstacle avoidance experiment in a dynamic unstructured environment for a robotic manipulator. The proposed method not only provides great practical engineering significance for a robotic manipulator’s obstacle avoidance in an intelligent factory, but also theoretical reference value for other type of robots’ path planning.
Highlights
In a future intelligent factory where the environment will be dynamic and unstructured, a robotic manipulator will work with humans efficiently and safely to complete a great variety of complex jobs and tasks collaboratively [1,2,3,4]
An autonomous obstacle avoidance dynamic path-planning method for a robotic manipulator based on an improved Rapidly Exploring Random Tree (RRT) algorithm, called Smoothly RRT (S-RRT), is proposed
Tree (RRT) algorithm based on random sampling has been widely applied in dynamic path planning for a high-dimensional robotic manipulator in a complex environment
Summary
In a future intelligent factory where the environment will be dynamic and unstructured, a robotic manipulator will work with humans efficiently and safely to complete a great variety of complex jobs and tasks collaboratively [1,2,3,4]. A dynamic path-planning method based on Probabilistic Roadmap (PRM) and RRT was presented when given a specific end effector task for a mobile manipulator, and was validated via a simulation to avoid static and dynamic obstacles efficiently, but was limited to simple cuboid and cylinder obstacles [13]. In this paper, a dynamic path-planning method for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm, called Smoothly RRT (S-RRT), is proposed to overcome the deficiencies reviewed above. The correctness, effectiveness, and practicability of the proposed method are demonstrated via a MATLAB static simulation and an ROS (Robot Operating System) dynamic simulation as well as a real autonomous obstacle avoidance experiment in a dynamic unstructured environment for a robotic manipulator
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