Energy Optimal Path Finding for Garbage Collection Robot using Ant Colony Optimization Algorithm

Abstract

Solid Waste Management (SWM) has always been an important consideration for any country, and among the operational steps of SWM, Solid Waste Collection (SWC) has become one of the most challenging ones. Currently, most of the vehicles used for waste collection require workers and have the problem of emitting CO2. Compared to waste collection by vehicles, waste collection using mobile robots has the advantage of not consuming personnel and not emitting CO2, which is harmful to the environment. However, while mobile robots can solve the shortage of manpower and environmental problems, they also have the problem of limited energy resources. In order for mobile robots to collect waste more efficiently, we designed the waste collection problem as a Capacitated Vehicle Routing Problem (CVRP) and optimized it using the Ant Colony Optimization (ACO) algorithm. The ACO algorithm proposed in this study focuses on the energy consumption of the mobile robot performing waste collection and searches for a route with less energy consumption by using the waste weight as the weighting factor. The preliminary performance verification of the proposed method is compared with the existing conventional ACO algorithm using the CVRP benchmark.