A Modified ACO with K-OPT for Restricted Covering Salesman Problems in Different Environments

In this study, the ant colony optimization (ACO) algorithm is modified with the K-opt operation to solve the covering salesman problem(CSP) under one restriction in crisp and imprecise (fuzzy, rough) environments. A CSP involves two phases- the division of cities into groups with the selection of the visiting cities and searching of the Hamiltonian circuit through the visiting cities. But, none of the studies in the literature is made following the direct approach. Also, none of the studies in the literature gives attention to reduce the total travel distance of the unvisited cities from the visited city of a group. Moreover, there is no algorithm in the literature which provides the solution of a CSP with the specified coverage range $r$. Also, none has introduced any algorithm to solve CSPs in imprecise environments. Though algorithms are available to solve the Traveling Salesman Problems in the imprecise environments, the approach cannot deal with the problems involving fuzzy data with non-linear membership functions or the problems involving rough data where the rough estimation can not be done using Lebesgue measure. The well establish algorithm for any routing problem is the ACO, but not much attention has been paid to solve the CSP using ACOs. To overcome these limitations on the studies of the ACO on the CSPs, here, an algorithm is proposed for the division of groups of the set of cities depending upon the maximum number of cities in a group and the total number of groups. Then ACO is used to find the shortest/minimum-cost path of the problem by selecting only one visiting the city from each group without violating the restriction of the specified coverage range $r$ of the location of the unvisited cities. K-opt operation is applied periodically at the end of ACO operation to improve the quality of the best found solution so far by the ACO algorithm and to arrest any premature convergence. For the restricted problems paths are searched in such a manner that the total distance/travel cost of different unvisited cities of a group from the visited city of the group should not exceed a predefined upper limit. To solve the problem in an imprecise environment some approach is followed so that the tour is searched without transferring the imprecise optimisation problem into an equivalent crisp optimisation problem. Also, the simulation approaches in fuzzy and rough environments are proposed to deal with the CSPs with any type of estimation of the imprecise data set. Algorithm is tested with the standard benchmark crisp problems available in the literature. To test the algorithm in the imprecise environments, the imprecise instances are derived randomly from the standard crisp instances using a specified rule. Test results imply that the proposed algorithm is efficient enough in solving the CSPs in the crisp as well as in the imprecise environments.

Coverage Motion Planning Using Graph in Forest Environment in Field Robot

This paper addresses the problem of using a mobile, autonomous robot to manage a forest whose trees are destined for eventual harvesting. We have been focussing a eliminate weeding operation because it is one of the hard work in the forestry works. This research proposing the computation of trajectory capable of traversing in the entire forest. The method is based on a graph whose vertices are trees located in the forest. Trees located in the forest will be treated as vertices in a graph. In the first, the initial graph is made with considering the safety of the robot. Next, editing the initial graph to be Eulerian, and finally, the Hamiltonian circuit is obtained which could be used for trajectory. By our proposed method, the trajectory of which feasible route for traversing of the entire forest would be obtained. In the experiment, we show the result of the method applying to actual artificial forest.

Compactly Folding Rigid Panels With Uniform Thickness Through Origami and Kirigami

In this paper, we investigate and evaluate origami and kirigami patterns that enable folding arrays made from flat rigid panels with uniform thickness into compact stacks. In deployed state, all panels form a completely flat plane; while in folded state, no voids exist within the stack. Two approaches are proposed. The first approach folds an array of identical rectangular panels into compact stacks. By drawing a Hamiltonian circuit over the array, a method of placing revolute joints is presented. By selecting a symmetric Hamiltonian circuit, we show that the array can be folded into two stacks. The second approach is case specific, which folds arrays consisting of square and half square triangular panels into stacks. Three basic units as well as their combinations are proposed, all of which lead to compact folding. Our designs can be applied to package solar panels for aerospace applications.

Robot Navigation in Forest Management

This paper addresses the problem of using a mobile, autonomous robot to manage a forest whose trees are destined for eventual harvesting. “Manage” in this context means periodical weeding between all the trees in the forest. We have constructed a robotic system enabling an autonomous robot to move between the trees without damaging them and to cut the weeds as it traverses the forest. This was accomplished by 1) computing a trajectory for the robot in advance of its entrance into the forest, and 2) developing a program and equipping the robot with the instruments needed to follow the trajectory. Computation of a trajectory in a forest is facilitated by treating the trees as vertices in a graph. Current, laser-based instruments make it possible to identify individual trees and compute distances between them. With this information a forest can be represented as a weighted graph. This graph can then be modified systematically in a way that allows for computing a Hamiltonian circuit that passes between each pair of trees. This representation is an instance of the well known Travelling Salesman Problem. The theory was put into practice in an experimental forest located at the Kyushu Institute of Technology. Our robot “SOMA,” built on an ATV platform, was able to follow a part of the trajectory computed for this small forest, thus demonstrating the feasibility of forest maintenance by an autonomous, labor saving robot.