The challenge for sustainable waste management begins from the sorting stage in homes, offices, and other public places, where trash is put into different bins - compost, recycle, and purely waste. While there are Materials Recycling Facilities (MRF) for sorting materials collected from the recycling bins, they are often ineffective due to “recycling contamination” from the sorting stage.
Assistant Professor Karthik Desingh (Computer Science and Engineering; MSI PI) is working on a project called “Sensing and Perceiving Recyclables for Robotic Sort-Empty-Clean-Drying Tasks,” that studies food waste contamination at the sorting stage (e.g., recyclable take-home food boxes, jars, and containers that have not been emptied or rinsed out). Not emptying, cleaning, and drying (ECD) such recyclables makes the recycling process less effective. This project aims to develop robotic technology to reduce food waste contamination and serve towards effective recycling. A robot with two arms can potentially sort trash following the ECD principles to maximize the value of recyclables. Professor Karthik and his group are aiming to develop such technology that would focus on sensing and perceiving the objects in the disposable stage, sorting them into respective categories, and emptying, cleaning, and drying recyclable categorical items before they go into the MRF. Part of the project is developing image segmentation algorithms that will take the image of the scene as input and output segments of waste categories.
This project recently received a Research Computing Seed Grant. RC Seed Grant funds are intended to promote, catalyze, accelerate and advance U of M-based informatics research in areas related to the MnDRIVE initiative, so that U of M faculty and staff are well prepared to compete for longer term external funding opportunities.
This Seed Grant falls under the Robotics research area of the MnDRIVE initiative.
Image description: An illustration of the proposed setup: A robot with two arms will be presented with a tray of trash. The robot observes the tray (a) and segments the items into different categories of waste (recyclable, trash, compost, etc.) (b). The robot further segments the regions on the recyclables to identify any contamination (c). Dual-arm manipulation allows for interactively segmenting the tray items as the robot performs sort-empty-clean-dry actions.
