Anuntachai, Anuntapat
Loading...
Preferred name
Anuntachai, Anuntapat
Main Affiliation
Email
anuntapat.an@kmitl.ac.th
2 results
Now showing 1 - 2 of 2
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, An application of ant algorithm for searching energy-efficient route a mobile robot takes using energy as a weighting factor(2014-12-10); ; A problem related to energy consumption of a mobile robot involves finding out what route the robot can take that uses the least energy. An ant colony optimization algorithm (ACO) can solve this problem. However, it is applicable only for route on a flat terrain. This paper proposes an adapted ant colony optimization (adapted ACO) algorithm that is applicable for route on a rough terrain as well. This adaptation introduces a weight that is the energy expended on a route that may have upward slopes, downward slopes, and flat surfaces. Experiments were conducted to test the algorithm. The experimental results show that our adapted ACO did successfully find a route that expended the least energy, though it was not the shortest one. We also found the following interesting facts: an energy-efficient route has more downward slopes than upward ones; the energy expended increases with the steepness of the slopes along a route; and the energy expended is likely to be lower if the robot’s velocity is not constrained to be constant throughout the route. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performance Measurement of Energy Optimal Path Finding for Waste Collection Robot Using ACO Algorithm(2022-01-01); ; ; Kuchii, ShigeruIndoor 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.