Integrating Simple Additive Weighting in Robotics Decision Support Systems

This study examines the application of the Simple Additive Weighting (SAW) method within a robotic Decision Support System (DSS) to enhance decision-making. The SAW method, known for its straightforward approach, was applied to evaluate alternatives based on criteria like sensor accuracy, energy efficiency, task completion time, safety, and adaptability. Each criterion was assigned a weight reflecting its importance. Alternatives representing different decision paths for a robotic system were assessed using these weighted criteria. The findings indicated that Alternative A5, which placed emphasis on adaptability and safety, was deemed the most appropriate option. The research, meanwhile, found many constraints of the SAW method, including its inherent subjectivity in assigning weights and its linear approach that may not be suitable for intricate and dynamic situations in the field of robotics. The results of this study indicate that the structured analytic workflow (SAW) provides a well-defined framework for decision-making. However, its effectiveness is enhanced when it is incorporated into a comprehensive decision support system (DSS) and combined with other dynamic approaches. This study emphasizes the possibilities and limitations of utilizing SAW in the context of robotic decision-making systems, thereby laying the groundwork for future progress in this area.