Energy optimal path finding for waste collection robot using ant colony optimization algorithm

Tomitagawa, Koki; Chotiphan, Supannada; Kuchii, Shigeru; Anuntachai, Anuntapat; Wongwirat, Olarn

doi:10.1109/icitee53064.2021.9611924

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…ACO is a meta-heuristic inspired by the behavior of ants when moving from their nests to their feeding sites. To solve the waste collection robot by using the conventional ACO algorithm as shown in the CVRP [7]- [9], [14], a total of k artificial ants, representing the robot, is placed in the graph of the optimization problem. Let N k i be the set of trash bins that have not been collected by artificial ant k when it is located at node i.…”

Section: A Aco Algorithm Deploymentmentioning

confidence: 99%

“…The probability p k ij that an artificial ant k located at node i will choose nodes j as its destination is calculated by the probability in (8) using two parameters: visibility and pheromone. p k ij(t) indicates the transition probability used for the t th iteration [14].…”

Section: A Aco Algorithm Deploymentmentioning

confidence: 99%

“…To prevent local optimization, the existing pheromone τij evaporates according to the evaporation rate ρ. The pheromone τij k to be applied between nodes i and j at the next iteration is updated as shown in (10) [14].…”

Section: A Aco Algorithm Deploymentmentioning

confidence: 99%

“…When SP is constructed with n trash bins, the total additional energy consumption Esp consumed by the robot up to that point can be shown by (14).…”

Section: Energy Consumption Modelmentioning

confidence: 99%

“…Our preliminary work in [14]- [15] considers the energyefficient path of the waste collection robot by using the adapted ACO algorithm, where either the path distance or the waste weight is used as the visibility, or path heuristic information, only. The aim of research work in this paper focuses on optimal energy consumption of the waste collection robot by proposing the ACO algorithm that employs a combination of the path distance and the waste weight as the path visibility.…”

Section: Introductionmentioning

confidence: 99%

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Performance Measurement of Energy Optimal Path Finding for Waste Collection Robot Using ACO Algorithm

et al. 2022

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Indoor waste collection that utilizes mobile robots can solve the labor cost and manpower shortage but has the problem of limited energy resources, making it difficult to operate for long periods of time. Therefore, it is important to reduce the energy consumption for efficient waste collection. The waste collection robot can be modeled as a Capacitated Vehicle Routing Problem (CVRP), where heuristics algorithms can be deployed to search for the most energy-efficient path. This paper proposes the Ant Colony Optimization (ACO) algorithm for finding the optimal path of the waste collection robot. Energy consumption of the robot depends not only on the travel path but also on the weight of the waste it carries. Therefore, the proposed ACO algorithm utilizes the path distance and waste weight as the visibility. The travel distance and energy consumption are also used to determine the updated pheromone. Whereas the conventional and adapted ACO algorithms use only either the path distance or the waste weight as the visibility, respectively. The simulation experiments are conducted to compare the travel distance and the energy consumption that the waste collection robot takes by using the conventional, adapted, and proposed ACO algorithms. In the simulation experiments, the number of nodes, the waste weight, and the carrying capacity are used as parameters to verify the performance under the determined environment. The simulation results express that the proposed ACO algorithm provides a better energy optimal path in terms of travel distance and energy consumption than the conventional and adapted ACO algorithms.

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Section: A Aco Algorithm Deploymentmentioning

confidence: 99%

Section: A Aco Algorithm Deploymentmentioning

confidence: 99%

Section: A Aco Algorithm Deploymentmentioning

confidence: 99%

“…When SP is constructed with n trash bins, the total additional energy consumption Esp consumed by the robot up to that point can be shown by (14).…”

Section: Energy Consumption Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations