Abstract
In recent days, the smart industry concept is becoming more prominent in industrial autonomous vehicle navigation systems. The need for service reliability and industrial management has led to various developing ideas in the industrial environment. Autonomous vehicles are commonly used for logistics and demand-aware migration of goods within the industry. In this article, the self-controlled touring and movement (SCTM) method is proposed to design, plan, and execute autonomous vehicle movements. The goal of this method is to improve the output of vehicle movements under controlled operating errors. For this purpose, the user requirement and outputs are identified and estimated beforehand for providing effective vehicle navigation plans. This error, balancing with the demands and outputs, is performed using classification learning wherein the changes of the vehicle's movements are frequently updated. Further changes are performed within the completion of the tour plan for retaining operational efficiency of the autonomous vehicle. This method is verified using the following metrics: percent of changes, operating error, delay, balancing factor, and response. The proposed SCTM reduces error by 9.73% and delay by 17.1%, whereas it increases the balancing factor by 8.3% for different tour instances.