A route and speed optimization model to find conflict-free routes for automated guided vehicles in large warehouses based on quick response code technology. (April 2022) - Li, Changmin; Zhang, Lu; Zhang, Liang
Highlights: An effective model has been established for conflict-free route and speed optimization of AGVs with the lowest energy consumption, namely RSOM. RSOM is for the AGVs in the warehouse that adopt Quick-Response (QR) code navigation system. The analysis of all node conflicts and arc conflicts provides a basis for solving path conflicts of large-scale AGVs. According to various schemes of adjusting speed and time, the branch and bound algorithm is constructed to effectively obtain optimal solution to RSOM of large-scale AGVs. The examples of AGVs using QR code navigation system have saved 30%-60% of the solving time as the scale increased, in comparison with path following techniques. Similarly, RSOM can save more time and energy, in comparison with path and time priority strategies. Abstract: This paper focuses on exploring conflict-free routes for automated guided vehicles (AGVs) based on quick response (QR) code technology. Intelligent warehousing has become an industrial development trend, with an increasing number of enterprises deploying AGVs based on QR code technology for transportation tasks, especially in large warehouses. Unlike the early single lane path following navigation systems, AGVs using QR code technology can freely switch tracks at any position. And two AGVs can be accommodated side by side. The conditions of conflict-free routes are analyzed firstly when AGVs use QR code navigation system. After that, a route and speed optimization model (RSOM) that aims at minimizing energy consumption and taking conflict-free routes with time windows as the constraint condition is established. Then a branch-and-bound (B&B) algorithm scheme is proposed by altering AGV routes or speed to arrive at schemes with conflict-free routes to complete transportation tasks. Lastly, the effectiveness and validity of our proposed model and algorithm are verified by numerical examples and a case study. The results show that resolving conflicts during route planning for AGVs guided through QR code have advantages over avoiding collision in the running phase by technology and path following techniques in both aspects of solution time and energy. In addition, the established B&B algorithm has better performance than the rapid-exploring random tree (RRT) algorithm in numerical examples. The gaps between them widen as the size of AGVs increases. The research work has potential for application to meet the requirements of AGV development.