Ahmet Yazici

An ultrasonic based indoor positioning system

Localization is an important problem for robotics and mobile platforms. Although there is some globally accepted positioning systems (e.g., GPS, GLONASS) for outdoor environments, there is not such a system for indoor. In this study, an ultrasonic based positioning system (SESKON) is developed especially for indoor robotic applications. The SESKON uses only ultrasonic signals, so it differs from other positioning systems that are developed for indoor. The SESKON uses Time Difference of Arrival (TDOA) technique for position calculation. TDOA is preferred since it does not require clock synchronization between receiver and transmitters. Tests are conducted to show the effectiveness of the developed SESKON system.

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 pattern-based genetic algorithm for multi-robot coverage path planning minimizing completion time

Sensor-based multi-robot coverage path planning problem is one of the challenging problems in managing flexible, computer-integrated, intelligent manufacturing systems. A novel pattern-based genetic algorithm is proposed for this problem. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a sequence of the disks for each robot to minimize the coverage completion time determined by the maximum time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel times. Robot turns cause the robot to slow down, turn and accelerate inevitably. Therefore, the actual travel time of a mobile robot is calculated based on the traveled distance and the number of turns. The algorithm is designed to handle routing and partitioning concurrently. Experiments are conducted using P3-DX mobile robots in the laboratory and simulation environment to validate the results.

A Low Cost Ultrasonic Based Positioning System for the Indoor Navigation of Mobile Robots

This paper presents an ultrasonic based indoor positioning (ICKON) system for indoor environments. The ICKON uses only ultrasonic signals and calculates the position of the mobile platform at centimeter-level accuracy. In this system, ultrasonic transmitters are located at known fixed positions and periodically send signals. The receiver is on the mobile unit and uses the time difference of arrival (TDOA) to calculate the mobile unit’s position. There is no synchronization signal between the transmitter and the mobile unit. The number of the mobile robots that can be used is restricted due to the physical limitations of the environment. Large indoor areas are covered by integrating an ultrasonic ID emission feature with the transmitters. The system uses the existing Local Area Network of the building, therefore it is cost effective. To demonstrate the effectiveness of this positioning system, a prototype is implemented and experiments are conducted using P3-DX mobile robots in various test environments.

Development of indoor positioning system with ultrasonic and infrared signals

Position measurement is one of the primal problems in wide range of applications such as air/land/marine vehicles tracking, automation systems, and equipment tracking in medical sector, robotics and sensor networks. Location information in outdoor application can be easily obtained from well-known systems such as GPS and GLONASS. Since GPS signal is not available in indoor area, several positioning systems have been developed for. But there is not a consensus for indoor positioning systems like the outdoor. In this study, a positioning system that uses ultrasonic and infrared signals is designed and implemented. The system is tested in 4m2 coverage range and it is observed that the maximum absolute error is less than +/-2 cm.

Pattern-Based Genetic Algorithm Approach to Coverage Path Planning for Mobile Robots

Sensor-based mobile robot coverage path planning (MRCPP) problem is a challenging problem in robotic management. We here develop a genetic algorithm (GA) for MRCPP problems. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a path which runs through the center of each disk at least once with minimal cost of full coverage. The proposed GA utilizes prioritized neighborhood-disk information to generate practical and high-quality paths for the mobile robot. Prioritized movement patterns are designed to generate efficient rectilinear coverage paths with no narrow-angle turn; they enable GA to find optimal or near-optimal solutions. The results of GA are compared with a well-known approach called backtracking spiral algorithm (BSA). Experiments are also carried out using P3-DX mobile robots in the laboratory environment.

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.

A Security-constrained Economic Power Dispatch Technique Using Modified Subgradient Algorithm Based on Feasible Values and Pseudo Scaling Factor for a Power System Area Including Limited Energy Supply Thermal Units

Abstract A security-constrained power dispatch problem with non-convex cost function for a lossy electric power system area including limited energy supply thermal units fueled under a take-or-pay agreement is formulated. Then, an iterative solution method based on a modified subgradient algorithm based on feasible values and common pseudo scaling factor for limited energy supply thermal units is proposed and used to solve the problem. In the iterative proposed solution method, a modified subgradient algorithm based on feasible values is used to solve the dispatch problem in each subinterval, while the common pseudo scaling factor for limited energy supply thermal units is employed to adjust the amount of fuel consumed by the limited energy supply units during the considered operation period. Since all equality and inequality constraints in the non-linear optimization model of a subinterval are functions of bus voltage magnitudes and phase angles (the off-nominal tap settings), they are taken as independent variables except for those belonging to the reference bus. Load flow equations are added to the model as equality constraints. The unit generation constraints, transmission line capacity constraints, bus voltage magnitude constraints, and off-nominal tap setting constraints are added into the optimization problem as inequality constraints. Since the modified subgradient algorithm based on feasible values requires that all inequality constraints should be expressed in equality constraint form, all inequality constraints are converted into equality constraints by the method given in this article before application of the modified subgradient algorithm based on feasible values to the optimization model. The proposed technique is tested on an IEEE 30-bus test system with a non-convex total cost rate curve. A portion of the dispatch problem considered in this article, where only a loading of the system in a subinterval is considered, was also solved by some other recently reported dispatch techniques. First, minimum total fuel cost rates and solution times obtained from the modified subgradient algorithm based on feasible values versus other techniques are compared, and the outperformance of the modified subgradient algorithm based on feasible values in terms of both total cost rate and solution time is demonstrated. After that, the optimal total fuel cost value and solution time of the system for the operation period for the cases where the fuel constraint is ignored and not ignored are presented, and it is shown that adding the fuel constraint into the dispatch problem further decreases the optimal total fuel cost for the considered dispatch problem. Finally, the modified subgradient algorithm based on feasible values was applied to the fuel constrained dispatch problem directly. It is demonstrated that the proposed solution method, where the modified subgradient algorithm based on feasible values is employed to solve an intervals dispatch problem, gives a solution time that is smaller than that obtained from the direct application of the modified subgradient algorithm based on feasible values to the dispatch problem.

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.