Optimization of motion and energy consumption of an industrial automated ground vehicle

Automated ground vehicles (AGVs) are means for performing a wide spectrum of tasks in the modern industries. The present paper addresses the problem of optimization of motion and energy consumption of such vehicles when they operate in a workspace with a warehouse and multiple workstations. A short description of the main attributes of AGVs is provided, followed by some references to their use as transport means for the distribution of resources in industrial environments. Next, their operation is considered by studying the energy consumption with respect to the load being carried and the resistances caused by their motion. Based on that study, a cost function is introduced, and an algorithm is also presented that considers the allocation tasks for the workstations and computes the least start load for the AGV. The heuristic search algorithm A* is used for the determination of optimal (least distance) paths for each combination of workstations’ pair. Then, a Genetic Algorithm (GA) locates optimal – in terms of distance and energy consumption – combinations/paths among workstations. The GA is supplied with two (2) different versions of fitness function that distinguish between multiple and unique pass of the AGV through workspace subspaces. The code has been implemented in Python and two case studies are presented and discussed. The proposed approach is innovative, presents low computational cost and may act as a tool that solves optimally the problem of motion and energy consumption for the AGVs by using efficiently artificial intelligence methods.