Inclusion of Rapidly Exploring Random Tree based Optimal Motion Planning Algorithm for 6-DOF Industrial Robots

抄録

Algorithms that can plan collision-free motions in a dynamic work environment rapidly and consistently are necessary to carry out dexterous operations with an industrial robot. For motion planning optimization, a variety of Rapidly-exploring Random Tree (RRT) based algorithms are used. This research studies a unique technique called flight cost-based RRT-star (FC-RRT*) for axis indexing tests with six degrees of freedom (6-DOF) robots. FC-RRT* is an enhanced version of classical RRT* that incorporates flight objective functions that contains the risk intensity and average distance between paths. This algorithm is devised to examine the link between different path nodes in an extensive setting. In contrast to the robot kinematic model, the results demonstrate that this applied technique can enhance the deviation in the motion planning algorithm. The FC-RRT* algorithm can be further applied in more convoluted situations with a variety of dynamic motion trajectories.