Anuntachai, Anuntapat
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Anuntachai, Anuntapat
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anuntapat.an@kmitl.ac.th
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Item type:Publication, Searching energy-efficient route in rough terrain for mobile robot with ant algorithm(2012-12-01); Recently, the ant algorithm has been widely used to solve the problem for searching optimized route from various different paths. It can be applied for searching an appropriate route that consumes less energy in mobile robot area as well. However, the previous optimized routes resulting from the ant algorithms considered only in flat terrain environment. They did not mention rough terrain environment. For the rough terrain, the optimized results might not be optimized in term of energy, due to slopes contained inside. This paper presents the application of ant algorithms for searching energy-efficient route of mobile robot in the rough terrain environment. The conventional ant colony optimization (ACO) algorithm and the adapted ACO algorithm are used to find the optimized routes in terms of distance and energy for comparison. The experimental results showed that, by using the speed with distance weighting factor, the adapted ACO yielded the optimized distance and energy in the flat terrain. In the rough terrain, the adapted ACO could also provide the energy-efficient routes better than the flat terrain. However, it could not be comparable with the ACO in some case and is required further improvement. © Springer-Verlag Berlin Heidelberg 2012. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Searching energy-efficient route for mobile robot with ant algorithm(2011-12-01); Recently, the ant algorithm has been widely used to solve the problem of searching for optimization route from various different paths. The ant algorithm can also be applied for searching an appropriate route that consumes less energy in mobile robot area, which is similar problem domain. However, in order to apply, the ant algorithm is required to adapt due to the factor of energy that must be considered in addition to the distance alone. This paper presents the adaptation of ant algorithm to solve the problem of searching energy-efficient route for mobile robot. The adapted ant algorithm deploys a speed, which is employed to find the energy to move the robot in each route, to generate pheromones used to define the probability that the ants will choose for the best route. Then, the distance is used as a weighting factor to discover the energy-efficient route in terms of distance and speed by simulation. The results from adapted ant algorithm are also used to compare with the conventional ant algorithm for investigation. The simulation results expressed that, when using the speed with the distance to weight, the average distance is shortest. Consequently, when transforming into the energy, the result is much lower as well. Therefore, the adaptation of ant algorithm can improve the result of searching problem for optimization route that consumes less energy, which is a limited resource of mobile robot. © 2011 ICROS. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Searching optimization route by using Pareto solution with ant algorithm for mobile robot in rough terrain environment(2016-01-01); ; A problem related to searching routes of a mobile robot involves finding the route that has the shortest distance and consumes the least energy, or an energy-efficient route. An ant colony optimization (ACO) algorithm can be used to solve this problem, but only on a flat terrain, since energy is depended on the distance. The adapted ACO can also be applied for searching the energy-efficient routes in the rough terrain, but it is difficult to achieve both criteria, simultaneously. In the rough terrain, the least energy route might not have the shortest distance. Also, the route having the shortest distance might not consume the least energy. In this scenario, an optimized route is required. This paper proposes a method to find the optimized route of a mobile robot in terms of energy and distance on the rough terrain by using a Pareto solution with adapted ACO algorithm. In the proposed method, the adapted ACO is applied for searching a set of routes that consumes the least energy. Then, the Pareto solution is deployed to find the optimized route in terms of energy and distance. The experiment was conducted by simulation to verify the proposed searching method. The experimental result shows that the optimized route having appropriate energy and distance can be found. It can be implied that this optimized route is the energy-efficient route in rough terrain environment. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adapted ACO Algorithm for Energy-Efficient Path Finding of Waste Collection Robot(2022-01-01); ; ; Kuchii, ShigeruWaste collection is a major concern of many companies with large areas of facility, e.g., buildings or factories, where there are many trash bins at various dumping points. Therefore, they require human labor to handle, which is a major cost of consideration. Currently, there are research works using robots for waste collection instead of humans. There is a challenge for waste collection robots in terms of energy consumption to pick up the waste at various dumping points efficiently. The factors related to the energy consumption of waste collection robots are directly related to the distance and waste weight that the robots have to collect and carry from the trash bins at various dump points along the paths. This paper presents the adapted ant colony optimization (ACO) algorithm to find the energy-efficient paths of the waste collection robots. The adapted ACO algorithm uses the waste weight in the trash bin as path heuristic information between two dumping points to determine the state transition probability for finding the most energy-efficient path. The experiment was conducted by the simulation to compare the result with the conventional ACO algorithm that uses distance as the path heuristic information. The simulation results expressed that the adapted ACO algorithm provided the most energy-efficient path under the number of nodes and waste weights specified better than the conventional ACO algorithm. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An application of Pareto solution with adapted ACO for searching optimal route of a mobile robot in rough terrain environment(2016-01-24); ; A challenge in searching an optimal route of a mobile robot involves finding the route that has the shortest distance and consumes the least energy. To solve this problem, an ant colony optimization (ACO) algorithm can be used, but only on a flat terrain, since the energy depends directly on the distance. In a rough terrain, the least energy route might not be the shortest distance. Also, the shortest distance route might not be the least energy. This is due to a factor of slope in the route. Although our adapted ACO can be used for searching energy-efficient routes in the rough terrain, it is difficult to achieve the shortest distance simultaneously. This paper proposes a novel method to find an optimal route of a mobile robot in rough terrain environment by using a Pareto solution with adapted ACO. In the proposed method, the adapted ACO is used to search two sets of route, i.e., one contains the least energy and another one contains the shortest distance. Then, the Pareto solution is deployed to find the optimal route in terms of energy and distance by adopting a distance vector for selection. The experiment was performed by simulation to verify the proposed searching method. The experimental results show that the proposed searching method can prescribe the optimal value for choosing the route provided by adapted ACO. - 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, Energy optimal path finding for waste collection robot using ant colony optimization algorithm(2021-01-01); ; 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. - 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.