Analysis of Multi-Objective Optimization Techniques for Robotic Arm Path Planning: A Systematic Literature Review

In the current industrial landscape, optimizing production processes and minimizing environmental impact are key priorities for industries. The integration of robotics into manufacturing processes has proven to be a significant advancement, primarily due to the ability of robots to reduce cycle times and maintain consistent repeatability in production. However, as the deployment of robotic arms in factories increases, so does their energy consumption. This rise in energy demand is related to the electric motors on each joint of the robots, the various sensors required for proper operation, and additional equipment such as custom grippers that may use compressed air. Moreover, the addition of more machines necessitates increased maintenance efforts to ensure continuous operation. Both preventive maintenance and corrective actions are crucial for minimizing costly production downtimes, as any interruption in the manufacturing line can lead to significant financial losses. The complexity of managing robotic movement, which involves multiple variables, further complicates the optimization of these processes. To address these challenges, multi-objective optimization (MOO) emerges as a powerful tool, providing a framework for balancing various conflicting objectives such as reducing energy consumption, minimizing wear on robot joints, and optimizing cycle times. The methodology employed in this research revolves around conducting a systematic literature review (SLR) to identify and analyze the most relevant studies in the fields of path planning, robotic manipulators, and multi-objective optimization (MOO). The SLR process is divided into several key steps, beginning with the identification of the research pillars, followed by the application of inclusion and exclusion criteria, and finally, the selection of the most pertinent papers for analysis. The research pillars - path planning, robotic manipulators, and MOO - serve as the foundation for this study. The inclusion and exclusion criteria are applied to ensure that only the most relevant and high-quality studies are considered. The criteria include language restrictions (English-only), peer-reviewed status, and a publication date cut-off (2019 or later). These filters help to narrow down the pool of articles from over 18,000 to a more manageable number. Further refinement is achieved by evaluating the titles and abstracts of the selected papers to eliminate any studies that do not directly contribute to the research objectives. The final step involves analyzing the selected articles to extract valuable insights into the methods and tools used in robotic path planning and optimization to understand why are certain software used to model these problems, and which are the algorithms used by other researchers to apply MOO in robotic scenarios. This analysis provides a basis for discussing the current state of research, identifying gaps, and proposing future research directions.