Path planning for industrial robots; Lazy Significant Edge Algorithm (LSEA)
This paper presents a new sampling based path planning algorithm, called the Lazy Significant Edge Algorithm (LSEA). LSEA utilises roadmap connectivity information to bias its sampling strategy towards objects in a robots workspace that have not yet been navigated by the robot. This allows LSEA to avoid redundant sampling of configuration space. The robotic system used in this paper to test LSEA consists of an articulated industrial manipulator mounted on a linear rail. LSEA was tested on this system with a series of different path planning problems in order to judge its overall effectiveness. When compared to a number of other popular sampling based path planning algorithms, it was concluded that LSEA had the best overall performance. It was observed to solve the various path planning problems more quickly than its counterparts, utilising fewer clash checks in order to reach the various solutions.