Aydin Sipahioglu

A Dynamic Path Planning Approach for Multirobot Sensor-Based Coverage Considering Energy Constraints

In this study, a novel dynamic path planning approach is proposed for multi-robot sensor-based coverage considering energy capacities of the mobile robots. The environment is assumed to be narrow and partially unknown. A Generalized Voronoi diagram-based network is used for the sensor-based coverage planning due to narrow nature of the environment. On the other hand, partially unknown nature is handled with proposed dynamic re-planning approach. Initially, the robots are assumed to be at the same depot with equal initial energy capacities. In this case, an initial complete coverage route is constructed considering robot energy capacities using classical capacitated arc routing problem (CARP) approach with some minor modifications related to coverage problem. But, due to partially unknown nature, the robots may face with blockage on routes, and a fast re-planning is required which considers remaining energy capacities and current positions of the robots. So, new plan is obtained by a modifying Ulusoys algorithm that was developed for classical CARP. The developed algorithm is coded in C++ and implemented on P3-DX mobile robots in MobileSim simulation environment.

Real-time tour construction for a mobile robot in a dynamic environment

Mobile robots are increasingly used in many areas. An optimum trajectory increases the effectiveness of a mobile robot. However, the environment may change dynamically which may require a real-time tour construction for the mobile robot. In this study, a heuristic-based TSP approach is applied to real-time dynamic tour construction problem for a mobile robot. Savings algorithm together with Dijsktras algorithm is used to determine a feasible tour for the mobile robot. The proposed method is applicable when the network is complete or sparse, directed or undirected. Experiments are conducted to show the effectiveness of the proposed algorithm.

A genetic algorithm for the quadratic multiple knapsack problem

The Quadratic Multiple Knapsack Problem (QMKP) is a generalization of the quadratic knapsack problem, which is one of the well-known combinatorial optimization problems, from a single knapsack to k knapsacks with (possibly) different capacities. The objective is to assign each item to at most one of the knapsacks such that none of the capacity constraints are violated and the total profit of the items put into the knapsacks is maximized. In this paper, a genetic algorithm is proposed to solve QMKP. Specialized crossover operator is developed to maintain the feasibility of the chromosomes and two distinct mutation operators with different improvement techniques from the non-evolutionary heuristic are presented. The performance of the developed GA is evaluated and the obtained results are compared to the previous study in the literature.

A multi-objective programming approach to 1.5-dimensional assortment problem

In this paper we study a 1.5-dimensional cutting stock and assortment problem which includes determination of the number of different widths of roll stocks to be maintained as inventory and determination of how these roll stocks should be cut by choosing the optimal cutting pattern combinations. We propose a new multi-objective mixed integer linear programming (MILP) model in the form of simultaneously minimization two contradicting objectives related to the trim loss cost and the combined inventory cost in order to fulfill a given set of cutting orders. An equivalent nonlinear version and a particular case related to the situation when a producer is interested in choosing only a few number of types among all possible roll sizes, have also been considered. A new method called the conic scalarization is proposed for scalarizing non-convex multi-objective problems and several experimental tests are reported in order to demonstrate the validity of the developed modeling and solving approaches.

A dynamic path planning approach for multi-robot sensor-based coverage considering energy constraints

In this study, a novel dynamic path planning approach is proposed for multi-robot sensor-based coverage considering energy capacities of the mobile robots. The environment is assumed to be narrow and partially unknown. A Generalized Voronoi diagram-based network is used for the sensor-based coverage planning due to narrow nature of the environment. On the other hand, partially unknown nature is handled with proposed dynamic re-planning approach. Initially, the robots are assumed to be at the same depot with equal initial energy capacities. In this case, an initial complete coverage route is constructed considering robot energy capacities using classical capacitated arc routing problem (CARP) approach with some minor modifications related to coverage problem. But, due to partially unknown nature, the robots may face with blockage on routes, and a fast re-planning is required which considers remaining energy capacities and current positions of the robots. So, new plan is obtained by a modifying Ulusoys algorithm that was developed for classical CARP. The developed algorithm is coded in C++ and implemented on P3-DX mobile robots in MobileSim simulation environment.

Multi-robot sensor-based coverage path planning using capacitated arc routing approach

In this study, a novel sensor-based coverage algorithm is proposed for multi-robots considering energy capacities of the mobile robots. Firstly, the environment is modeled by a Generalized Voronoi diagram-based graph to guarantee complete sensor based coverage. Secondly, depending on required arc set, an initial complete coverage route is created by using Chinese Postman Problem (CPP) and/or Rural Postman Problem (RPP). Then this initial route is partitioned among robots using Ulusoys algorithm, which was developed for basic capacitated arc routing (CARP), by considering robot energy capacities. Although the multi-robot sensor-based coverage problem resembles CARP, there are some differences. Therefore, Ulusoys algorithm is modified and used for this problem. The developed algorithm is coded in C++ and implemented on P3-DX mobile robots in MobileSim simulation environment.

Generalized quadratic multiple knapsack problem and two solution approaches

The Quadratic Knapsack Problem (QKP) is one of the well-known combinatorial optimization problems. If more than one knapsack exists, then the problem is called a Quadratic Multiple Knapsack Problem (QMKP). Recently, knapsack problems with setups have been considered in the literature. In these studies, when an item is assigned to a knapsack, its setup cost for the class also has to be accounted for in the knapsack. In this study, the QMKP with setups is generalized taking into account the setup constraint, assignment conditions and the knapsack preferences of the items. The developed model is called Generalized Quadratic Multiple Knapsack Problem (G-QMKP). Since the G-QMKP is an NP-hard problem, two different meta-heuristic solution approaches are offered for solving the G-QMKP. The first is a genetic algorithm (GA), and the second is a hybrid solution approach which combines a feasible value based modified subgradient (F-MSG) algorithm and GA. The performances of the proposed solution approaches are shown by using randomly generated test instances. In addition, a case study is realized in a plastic injection molding manufacturing company. It is shown that the proposed hybrid solution approach can be successfully used for assigning jobs to machines in production with plastic injection, and good solutions can be obtained in a reasonable time for a large scale real-life problem.

Heuristic solution approaches for the cumulative capacitated vehicle routing problem

Cumulative capacitated vehicle routing problem (CCVRP) is an extension of the well-known capacitated vehicle routing problem, where the objective is minimization of sum of the arrival times at nodes instead of minimizing the total tour cost. This type of routing problem arises when a priority is given to customer needs or dispatching vital goods supply after a natural disaster. This paper focuses on comparing the performances of neighbourhood and population-based approaches for the new problem CCVRP. Genetic algorithm (GA), an evolutionary algorithm using particle swarm optimization mechanism with GA operators, and tabu search (TS) are compared in terms of required CPU time and obtained objective values. In addition, a nearest neighbourhood-based initial solution technique is also proposed within the paper. To the best of authors’ knowledge, this paper constitutes a base for comparisons along with GA, and TS for further possible publications on the new problem CCVRP.

Capacitated arc routing problem with deadheading demands

Capacitated arc routing problem (CARP) is the determination of vehicle tours that serve all positive-demand edges (required edge) exactly once without exceeding vehicle capacity while minimizing sum of all tour costs. In CARP, total demand of a tour is calculated by means of all required edges on the tour. In this study, a new CARP variation is introduced, which considers not only required edges but also traversed edges while calculating total demand of the tour. The traversing demand occurs when the traversed edge is either servicing or non-servicing (deadheading). Since the new CARP formulation incurs deadheading edge demands it is called CARP with deadheading demands. An integer linear model is given for the problem which is used to solve small-sized instances, optimally. A constructive heuristic is presented to solve the problem which is a modified version of a well-known CARP heuristic. Furthermore, two post-optimization procedures are presented to improve the solution of the heuristic algorithm. The effectiveness of the proposed methods is shown on test problems, which are obtained by modifying CARP test instances.

Heuristic-Based Dynamic Route Planning Method for a Homogeneous Multi-robot Team

Multi-robot systems have recently received a great deal of attention due to the ability to perform an assigned task in a more reliable, faster and cheaper way beyond what is possible with a single robot. However, they may have some drawbacks, such as obstruction among robots during a task. Non-intersecting tours are preferable for the robots to prevent obstruction. Moreover, after an initial plan, the environment (open roads may be closed) and/or task requests (active/deactive) may change dynamically, which may require a fast tour construction for the members of the mobile robot group. In this study, a novel heuristic method is proposed to construct non-intersecting tours for the members of a mobile robot group in dynamic and/or partially unknown environments considering the energy capacities of the robots. It is an aggregate algorithm consisting of the Savings algorithm and the Sweep algorithm, and is applicable to the problems that are modeled using complete, sparse, directed or undirected networks. Simulations are offered to show the effectiveness of the proposed algorithm.