Comparison of RRT and TRRT Object Arrangement Path Planning Robot for Retail Warehouse Using CoppeliaSim

Abstract

Shelf stocking in retail warehouses requires high accuracy, multitasking, and precise timing. Automating the process has become a popular solution due to difficulty in facing continuous demand in replenishing items at shelves. The traditional path planning, Rapid-exploring Random Tree (RRT) approach is inefficient and produce suboptimal path generation when it comes to complex workspaces such as retail warehouse. Furthermore, the simple linear interpolation method used for robotic arm manipulation can result in jerky, unrealistic motion, and collisions with obstacles. To address these issues, the Transition-based Rapidly-exploring Random Tree Star (TRRT) is proposed to achieve the shortest distance in shelving items at warehouse. The study also implements inverse kinematics algorithms for smooth arm manipulation. In this study, the TRRT is constructed in CoppeliaSim software using the OMPL Library as well as Inverse Kinematics (IK) algorithms. The results show that the TRRT approach generates shorter paths than the RRT approach but takes longer calculation time. Moreover, the TRRT approach demonstrates a good repetition result compared to the RRT approach. Furthermore, the accuracy percentage between the joints angle obtained from CoppeliaSim and the IK calculation in MATLAB reaches 99.27%. In conclusion, the TRRT approach and inverse kinematics algorithms improve the efficiency and smoothness of the object arrangement path planning robot, making it a viable solution for automating the shelf-stocking process in retail warehouses.