Many studies have used different optimisation methods to find a near-optimal solution by optimising the disassembly operations sequence. These studies have used disassembly operation time as the main optimisation parameter, and other parameters such as direction change or tool change are converted to time scale. In order to determine accurate operation time, a product needs to be completely disassembled, noting that the same EOL products can be in a different condition and result in different operation time. In this work, new optimisation parameters based on the disassemblability and components demand are defined. These include Disassembly Handling Index (DHI), Disassembly Operation Index (DOI) and Disassembly Demand Index (DDI). In order to consider the operation time and other costs, Disassembly Cost Index (DCI) is further defined. Genetic algorithm optimisation method was employed to optimise the process sequence. Here, the most demanded components with the easiest disassembly operations are disassembled first without requiring to disassemble the unwanted components and avoid complicated operations. Two case studies were analysed to determine the effectiveness and compatibility of this method. The result shows 13% and 10% improvement in overall disassembly time for the case studies.
This paper provides an overview of the current state‐of‐the‐art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labor costs, and minimize wastage by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning, and control. The paper also discusses the potential benefits of integrating artificial intelligence and soft robots and data‐driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.