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Author: search.filters.author.Thammano, AritSubject: search.filters.subject.Mobile robot

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    An application of Pareto solution with adapted ACO for searching optimal route of a mobile robot in rough terrain environment
    (2016-01-24)
    Anuntachai, Anuntapat
    ;
    Thammano, Arit
    ;
    Wongwirat, Olarn
    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.
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    An application of ant algorithm for searching energy-efficient route a mobile robot takes using energy as a weighting factor
    (2014-12-10)
    Anuntachai, Anuntapat
    ;
    Wongwirat, Olarn
    ;
    Thammano, Arit
    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.