Sorting of recyclables from source-segregated municipal solid waste (MSW) stream is an essential step in the recycling chain in a material recovery facility (MRF) for waste management. Manual sorting of recyclables in an MRF is a highly hazardous operation for human health as well as time-consuming. Application of robotics for automated waste sorting can alleviate these problems to a large extent. The total sorting time depends upon the pick-and-place (PAP) sequence used in a robotic sorting system. In this context, the generation of optimal PAP sequence plan is a key challenge considering that it cannot be solved by an exhaustive search due to the combinatorial explosion of the search-space. This paper reports an approach for generating optimal PAP sequence plan for robotic sorting of recyclables from source-segregated MSW stream in a system equipped with thermal-imaging technique. The PAP sequence generation is formulated as an optimization problem wherein the objective is to minimize the total sorting time. The formulated problem has been solved using a genetic algorithm (GA)- based approach. Numerical simulations as well as physical experiments using a 6-DOF articulated manipulator have been performed to test and validate the developed optimal sequence generation algorithm. Results revealed an improvement of up to 4.28% speedup in total sorting time over that of randomly generated sequences. It is envisaged that the developed approach can substantially improve the sorting performance in an MRF.
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.