A.N.T.s: Algorithm for Navigating Traffic System in Automated Warehouses
According to CNN Indonesia 2020, the demand for e-Commerce in Indonesia has nearly doubled during this pandemic. This surge in demand calls for a time-efficient method for warehouse order-picking. One approach to achieve that goal is by incorporating automation in their warehouse systems. Globally, the market of warehouse robotics is expected to reach 12.6 billion USD by 2027 (Data Bridge Market Research, 2020). In this research, the warehouse system studied would utilize AMR (Autonomous Mobile Robots) to lift and deliver movable shelf units to the packing station where workers are at. This research designed a heuristic algorithm called A.N.T.s (Algorithm for Navigating Traffic System) to conduct task assigning and pathfinding for AMR in the automated warehouse. The warehouse layout was drawn as a two-dimensional map in grids. When an order is placed, A.N.T.s would assign the task to a robot that would require the least amount of time to reach the target shelf. A.N.T.s then conducted pathfinding heuristically using Manhattan Distance. A.N.T.s would help the robot to navigate its way to the target shelf unit, lift the shelf and bring it to the designated packing station. A.N.T.s algorithm was tested in various warehouse layouts and with a varying number of AMRs. Comparison against the commonly used Djikstra’s algorithm was also conducted (Shaikh and Dhale, 2013). Results show that the proposed A.N.T.s algorithm could execute 100 orders in a 27x23 layout with five robots 9.96 times faster than Dijkstra with no collisions. The algorithm is also shown to be able to help assign tasks to robots and help them find short paths to navigate their ways to the shelf units and packing stations. A.N.T.s could navigate traffic to avoid deadlocks and collisions in the warehouse with the aid of lanes and directions.
HOPE WASTE (House Processor Waste) with IoT (Internet of Things) as a Laundry Liquid Waste Treatment Household Environment
Washing is one of the things that must be done by every household. Rural and urban communities have to wash clothes every day, to get clean clothes so they can be reused. But it turns out that with many households doing this activity, it will cause side effects that are not good. The impact will worsen the quality of the surrounding water because this activity is not equipped with a waste treatment process, but instead is dumped directly into the nearest ditch or river. As a result, this waste causes water pollution. The chemical compositions contained in detergents are grouped into 3, namely surface active substances ranging from 20-30%, reinforcing agents are the largest detergent components ranging from 70-80% and other ingredients around 2-8%, where surfactants are the main ingredients. cleaning agent in detergent. If not managed properly, it will cause environmental problems in the future. This research was carried out for 4 months at MAN Sidoarjo and Brawijaya University. The research method used was research and development and experiment methods, and data collection techniques using the observation method. From these problems, we offer a solution by making an internet of things-based device which we call HOPE WASTE (House Processor Waste) with IoT (Internet of Things) as the processing of household laundry liquid waste. HOPE Waste is a house-shaped device that functions to treat Laundry Liquid Waste which combines electrocoagulation methods and utilizes Biosorbents, namely Barringtonia Asiatica and Activated Charcoal which are made into powder. Where the Biosorbent content can bind chemicals in laundry liquid waste so that we can combine them using environmentally friendly IoT-based electrocoagulation methods.