Abstract
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 degrees-of-freedom (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.
The Study about the Solid Waste Automatic Sorting System at the Source--Take Beijing for Example
