DEVELOPING ENVIRONMENTALLY ADAPTED SIMULATORS FOR AUTONOMOUS MINING DUMP TRUCKS: A MULTI-CRITERIA APPROACH TO ENHANCE SUSTAINABILITY AND ECOLOGICAL SAFETY

The study explores the selection and development of simulators tailored for autonomous mining dump truck control systems, with a focus on sustainability and environmental adaptation.Mining operations pose unique challenges due to extreme weather conditions, varied terrain, and restricted visibility, which necessitate specialized simulators to ensure effective and eco-friendly solutions.By leveraging multi-criteria analysis, this research evaluates simulator capabilities to optimize resource use, minimize ecological risks, and enhance operational safety.The use of simulation environments mitigates real-world risks such as emissions, environmental contamination, and damage to ecosystems, while enabling efficient training and testing of autonomous systems.Additionally, the study underscores the importance of integrating ecosystem-specific parameters into simulation models to address climate variability and reduce the carbon footprint of development processes.The findings provide actionable insights for designing sustainable and environmentally conscious autonomous control systems in the mining industry.