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EN
A simple and efficient method for creating a motion trajectory is presented with an aim to achieve sufficient coverage of a given terrain. A chaotic map has been used in order that the motion trajectory should be unpredictable. The chaotic path generator which has been created, is used for implementing a robot’s movement in four and eight directions. The path generator is tested in various scenarios and the results are discussed. After thorough examination, the proposed method shows that the motion in eight directions gives better and very satisfactory results.
2
Content available remote Application of the ACO algorithm for UAV path planning
EN
The ACO (Ant Colony Optimization) algorithm is a bio-inspired metaheuristic used to optimize problems or functions described by graphs, sequences of events, or queues of tasks. It is used, among a variety of other purposes, when routing Internet network packets, determining the shortest routes between designated points (traveling salesman's problem), for the time and cost optimization of production, or setting public transport stops. In the article, the ACO algorithm was used to autonomously construct the optimal route for an unmanned aerial vehicle (UAV). The algorithm establishes the spatial orientation of the UAV, indicating the direction of its transition for each intermediate waypoint. The results of the simulations show the trajectory of the UAV depending on the selected weighting factors, determining the priority of avoiding detected hazards or choosing the shortest path. The quality of each variant is evaluated numerically by the calculated fitness function, the value of which is the sum of the costs of the transition to each intermediate route point. The effect of the algorithm is a set of executable trajectory variants, of which the one with the best fitness value is selected.
PL
Algorytm ACO (ang. Ant Colony Optimization) jest bio-inspirowaną metaheurystyką, wykorzystywaną do optymalizacji problemów lub funkcji opisywanych za pomocą grafów, sekwencji zdarzeń, czy też kolejki zadań. Znajduje on zastosowanie m.in. przy trasowaniu pakietów sieci internetowych, wyznaczaniu najkrótszych tras między wyznaczonymi punktami (problem komiwojażera), optymalizacji czasu i kosztu produkcji, czy też ustalaniu przystanków transportu publicznego. W artykule, algorytm ACO został wykorzystany do autonomicznego wyznaczenia optymalnej trasy dla bezpilotowego statku powietrznego (BSP). Algorytm ustala orientację przestrzenną BSP, determinującą kierunek jego przemieszczenia dla każdego pośredniego punktu docelowego. Wyniki przeprowadzonych symulacji przedstawiają trajektorię BSP w zależności od dobranych współczynników wagowych, określających priorytet ominięcia wykrytych zagrożeń lub wybrania najkrótszej drogi. Jakość każdego wariantu jest określana liczbowo poprzez ustaloną funkcję dopasowania, której wartość stanowi suma kosztów przejścia do każdego pośredniego punktu trasy. Efektem działania algorytmu jest zbiór wykonywalnych wariantów trajektorii, z których wybrany zostaje ten o najlepszej wartości dopasowania.
EN
The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.
4
EN
Driving a road vehicle is a very complex task in terms of controlling it, substituting a human driver with a computer is a real challenge also from the technical side. An important step in vehicle controlling is when the vehicle plans its own trajectory. The input of the trajectory planning are the purpose of the passengers and the environment of the vehicle. The trajectory planning process has several parts, for instance, the geometry of the path-curve or the speed during the way. Furthermore, a traffic situation can also determine many other parameters in the planning process. This paper presents a basic approach for trajectory design. To reach the aim a map will be given as a binary 2204 x 1294 size matrix where the roads will be defined by ones, the obstacles will be defined by zeros. The aim is to make an algorithm which can find the shortest and a suitable way for vehicles between the start and the target point. The vehicle speed will be slow enough to ignore the dynamical properties of the vehicle. The research is one of the first steps to realize automated parking features in a self-drive car.
5
Content available An exact geometry-based algorithm for path planning
EN
A novel, exact algorithm is presented to solve the path planning problem that involves finding the shortest collision-free path from a start to a goal point in a two-dimensional environment containing convex and non-convex obstacles. The proposed algorithm, which is called the shortest possible path (SPP) algorithm, constructs a network of lines connecting the vertices of the obstacles and the locations of the start and goal points which is smaller than the network generated by the visibility graph. Then it finds the shortest path from start to goal point within this network. The SPP algorithm generates a safe, smooth and obstacle-free path that has a desired distance from each obstacle. This algorithm is designed for environments that are populated sparsely with convex and nonconvex polygonal obstacles. It has the capability of eliminating some of the polygons that do not play any role in constructing the optimal path. It is proven that the SPP algorithm can find the optimal path in O(nn’2) time, where n is the number of vertices of all polygons and n’ is the number of vertices that are considered in constructing the path network (n’ ≤ n). The performance of the algorithm is evaluated relative to three major classes of algorithms: heuristic, probabilistic, and classic. Different benchmark scenarios are used to evaluate the performance of the algorithm relative to the first two classes of algorithms: GAMOPP (genetic algorithm for multi-objective path planning), a representative heuristic algorithm, as well as RRT (rapidly-exploring random tree) and PRM (probabilistic road map), two well-known probabilistic algorithms. Time complexity is known for classic algorithms, so the presented algorithm is compared analytically.
PL
Pomimo znacznych postępów w tematyce zwiększenia autonomiczności bezzałogowych obiektów latającego, pozostaje jeszcze wiele problemów do rozwiązania, jednym z nich to problem autonomicznego planowania trasy. Mimo iż ten problem jest obecnie przedmiotem badań licznych ośrodków badawczych na świecie, nadal jednak nie opracowano uniwersalnego sposobu planowania trasy, gdyż jest to związane nie tylko z właściwościami danego obiektu, ale również z realizowaną misją. W niniejszym artykule omówiono problem planowania trasy dla bezzałogowego statku powietrznego podczas lotu nad terenem z przeszkodami. Opracowany został algorytm do wyznaczania trasy uwzględniający ograniczenia nałożone przez właściwości obiektu latającego, ukształtowanie terenu, strefy zakazane oraz maksymalny dopuszczalny pułap lotu. Ponadto zaproponowano metodę poszukiwania quasi-optymalnej trajektorii w przypadku większej liczby przeszkód. Przeprowadzono szereg badań symulacyjnych weryfikujących poprawność działania opracowanego algorytmu.
EN
Despite significant progress in the field of increasing the autonomy of unmanned aerial vehicles (UAVs), there are still a number of problems which needs to be solved. One of such example is the problem of autonomous path planning. In this paper, the problem of UAV path planning in mountainous terrain with obstacles has been discussed. AUAV path planning algorithm that takes into account limitations imposed by UAVs dynamics, terrain configuration, no-fly zones and the maximum allowable flight altitude has been developed. Furthermore, the method of searching for the quasi-optimal path in the case of multiple obstacles has been proposed. A series of simulation investigations to verify the correctness of developed algorithm have been carried out.
EN
Various methods of trajectory determination are used for finding solutions to collision situations involving ships. This applies to avoiding collisions with other ships or stationary objects. In addition to the methods generally used, new or modified versions of methods derived from other modes of transport are proposed. One of the algorithms for route determination serving to avoid obstructions is the method of artificial potential fields, used for determining routes of mobile robots. The method is used in maritime transport, for instance for detecting anomalies in ship movement. The article presents the method of potential fields used for solving the problem of route selection avoiding navigational dangers and obstacles. This article presents an algorithm of route determination based on the said method, its implementation in the MATLAB program and examples of application for the ship’s safe trajectory determination in some navigational situations.
EN
This paper presents a constrained Particle Swarm Optimization (PSO) algorithm for mobile robot path planning with obstacle avoidance. The optimization problem is analyzed in static and dynamic environments. A smooth path based on cubic splines is generated by the interpolation of optimization solution; the fitness function takes into consideration the path length and obstaclegenerated repulsive zones. World data transformation is introduced to reduce the optimization algorithm computational complexity. Different scenarios are used to test the algorithm in simulation and real-world experiments. In the latter case, a virtual robot following concept is exploited as part of the control strategy. The path generated by the algorithm is presented in results along with its execution by the mobile robot.
PL
W artykule przedstawiono algorytm rojowy z ograniczeniami realizujący planowanie bezkolizyjnej ścieżki ruchu robota mobilnego. Problem optymalizacyjny został przeanalizowany dla środowiska statycznego i dynamicznego. Do stworzenia gładkiej ścieżki ruchu wykorzystano interpolację rozwiązania optymalizacji przy użyciu sześciennych funkcji sklejanych. Funkcja kosztu uwzględnia długość ścieżki ruchu oraz penalizację za naruszenie przestrzeni przeszkód. Wprowadzono transformację świata w celu redukcji złożoności obliczeniowej algorytmu optymalizacji. Przeprowadzono zróżnicowane scenariusze badawcze testujące algorytm w eksperymentach symulacyjnych i rzeczywistych. W przypadku tych ostatnich wykorzystano ideę podążania za wirtualnym robotem. Zaprezentowano wyniki obrazujące wygenerowaną ścieżkę ruchu oraz ocenę jej realizacji przez robota mobilnego.
9
EN
This paper is concerned with the optimal path planning for reduction in residual vibration of two-flexible manipulator. So after presenting the model of a two-link flexible manipulator, the dynamic equations of motion were derived using the assumed modes method. Assuming a desired path for the end effector, the robot was then optimized by considering multiple objective functions. The objective functions should be defined such that in addition to guaranteeing the end effector to travel on the desired path, they can prevent the undesirable extra vibrations of the flexible components. Moreover, in order to assure a complete stop of the robot at the end of the path, the velocity of the end effector at the final point in the path should also reach zero. Securing these two objectives, a time-optimal control may then be applied in order for the robot to travel the path in the minimum duration possible. In all the scenarios, the input motor torques applied to the Two-link are determined as the optimization variables in a given range. The optimization procedures were carried out based on the GA (Genetic Algorithm) and BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithms, and the results are then compared. It is observe that the BFGS algorithm was able to achieve better results compared to GA running a lower number of iterations. Then the final value of the objective function after optimization indicates the decrease in the vibrations of the end effector at the tip of the flexible link.
EN
Optimization algorithms use various mathematical and logical methods to find optimal points. Given the complexity of models and design levels, this paper proposes a heuristic optimization model for surface-to-air missile path planning in order to achieve the maximum range and optimal height based on 3DOF simulation. The proposed optimization model involves design variables based on the pitch programming and initial pitch angle (boost angle). In this optimization model, we used genetic and particle swarm optimization (PSO) algorithms. Simulation results indicated that the genetic algorithm was closer to reality but took longer computation time. PSO algorithm offered acceptable results and shorter computation time, so it was found to be more efficient in the surface-to-air missile path planning.
EN
We present a novel method of fast and reliable data gathering for the purpose of location services based on radio signal strength services such as WiFi location/ navigation. Our method combines the acquisition of location and mapping based on computer vision methods with WiFi signal strength stochastic data gathering. The output of the method is threefold: 3D metric space model, 2D floor plan map and metric map of stochastic radio signal strength. The binding of location data with radio data is done completely automatically, without any human intervention. The advantage of our solution lies also in a significant speed-up and density increase of Radio Map generation which opens new markets for WiFi navigation services. We have proved that presented solution produces a map allowing location in office space of accuracy 1.06 m.
EN
Monitoring of biological and chemical pollutants in large bodies of water requires the acquisition of a large number of in-situ measurements by a mobile sensor platform. Critical to this problem is an efficient path planning method, easily adaptable to different control strategies that ensure the collection of data of the greatest value. This paper proposes a deliberative path planning algorithm, which features the use of waypoints for a ship navigation trajectory that are generated by Genetic Algorithm (GA) based procedures. The global search abilities of Genetic Algorithms are combined with the heuristic local search in order to implement a navigation behaviour suitable to the required data collection strategy. The adaptive search system operates on multi-layer maps generated from remote sensing data, and provides the capacity for dealing with multiple classes of water pollutants. A suitable objective function was proposed to handle different sampling strategies for the collection of samples from multiple water pollutant classes. A region-of-interest (ROI) component was introduced to deal effectively with the large scale of search environments by pushing the search towards ROI zones. This resulted in the reduction of the search time and the computing cost, as well as good convergence to an optimal solution. The global path planning performance was further improved by multipoint crossover operators running in each GA generation. The system was developed and tested for inland water monitoring and trajectory planning of a mobile sample acquisition platform using commercially available satellite data.
EN
Autonomous underwater vehicles are vehicles that are entirely or partly independent of human decisions. In order to obtain operational independence, the vehicles have to be equipped with specialized software. The task of the software is to move the vehicle along a trajectory while avoiding collisions. In its role of avoiding obstacles, the vehicle may sometimes encounter situations in which it is very difficult to determine what the next movement should be from an ad hoc perspective. When such a situation occurs, a planning component of the vehicle software should be run with the task of charting a safe trajectory between nearby obstacles. This paper presents a new path planning algorithm for a Biomimetic Autonomous Underwater Vehicle. The main distinguishing feature of the algorithm is its high speed compared with such classic planning algorithms as A*. In addition to presenting the algorithm, this paper also summarizes preliminary experiments intended to assess the effectiveness of the proposed algorithm.
EN
This paper presents optimisation of a measuring probe path in inspecting the prismatic parts on a CMM. The optimisation model is based on: (i) the mathematical model that establishes an initial collision-free path presented by a set of points, and (ii) the solution of Travelling Salesman Problem (TSP) obtained with Ant Colony Optimisation (ACO). In order to solve TSP, an ACO algorithm that aims to find the shortest path of ant colony movement (i.e. the optimised path) is applied. Then, the optimised path is compared with the measuring path obtained with online programming on CMM ZEISS UMM500 and with the measuring path obtained in the CMM inspection module of Pro/ENGINEER® software. The results of comparing the optimised path with the other two generated paths show that the optimised path is at least 20% shorter than the path obtained by on-line programming on CMM ZEISS UMM500, and at least 10% shorter than the path obtained by using the CMM module in Pro/ENGINEER®.
EN
A path planning problem for a heterogeneous vehicle is considered. Such a vehicle consists of two parts which have the ability to move individually, but one of them has a shorter range and is therefore required to keep in a close distance to the main vehicle. The objective is to devise an optimal path of minimal length under the condition that at least one part of the heterogeneous system visits all desired waypoints exactly once. Two versions of the problem are considered. One assumes that the order in which the waypoints are visited is known a priori. In such a case we show that the optimal path can be found by solving a mixed-integer second-order cone problem. The second version assumes that the order in which the waypoints are visited is not known a priori, but can be optimized so as to shorten the length of the path. Two approaches to solve this problem are presented and evaluated with respect to computational complexity.
PL
W artykule omówiony został problem wykorzystania planerów podróży w zastosowaniach sportowych, turystycznych i rekreacyjnych. Przedmiotowy problem zilustrowano na przykładzie wykorzystania planera podróży o nazwie Green Travelling Planner (GT Planner). Każdy planer podroży służy do wyznaczenia ścieżki przemieszczenia w sieci transportowej pomiędzy punktem początkowym podróży, a końcowym. Tą funkcjonalność planerów opisano w przedmiotowym artykule w aspekcie planowania tras turystycznych i rekreacyjnych oraz interesujących aspektów wykorzystania w sporcie. Zasadniczy problem w przystosowaniu planera podroży do celów: sportowych, turystycznych i rekreacyjnych tkwi w wykorzystywanych przez niego źródłach danych. Problem ten szczegółowo opisano w niniejszym artykule. Podstawową funkcjonalnością planera zaproponowanego do realizacji przemieszczeń: sportowych, rekreacyjnych i turystycznych o nazwie GT Planner jest indeksowanie przemieszczeń wielkością emisji substancji szkodliwych jaka jest z jego realizacją związana. W połączeniu z innymi funkcjonalnościami tego narzędzia, takimi jak wykorzystywanie danych o profilach wysokości, wykorzystanie funkcji heurystycznych cechujących sieć transportową, można taki planer wykorzystać w specyficznych zagadnieniach: sportowych, turystycznych i rekreacyjnych. Na rynku jest obecnie kilka planerów podróży, które można określić mianem stricte turystycznych, z uwagi na co zostaną one opisane obok prezentowanego narzędzia GT Planner.
EN
The article discussed the problem of the use trip planners for sports, tourist and recreational. The present problem exemplified use for this purpose route planner called Green Travelling. Each journey planner is used to determine the path in the transport network between the starting point of the journey, and the end. This functionality planners described in this article in terms of planning hiking trails and recreational facilities. The fundamental problem in adapting journey planner for sports, tourism and recreation is in used by its data sources. This problem is described in detail in this article. The basic functionality of the proposed agenda for the realization movements sports, recreational and tourist called Green Travelling is indexing the displacement size of the emission of harmful substances which is associated with them (trip route). In combination with other functionalities of this tool such as the use of profiles height, the use of heuristics characterize transport network can be a planer used in specific issues of sports, tourist and recreational activities. Currently on the market is several travel planners that can be described as a tourist, because of what was on themcompared to the tool Green traveling.
PL
Transport wiąże się z przemieszczaniem osób, ładunków w przestrzeni przy wykorzystaniu odpowiednich środków transportu. Potrzeby te należą do grupy potrzeb wtórnych człowieka i są związane z faktem różnego rozmieszczenia przestrzennego zasobów, skupisk ludzkich i miejsc pracy. Transport towarzyszył ludzkości od samych początków rozwoju cywilizacji. Jest to, obok łączności, dział gospodarki, które zwiększają użyteczność dóbr poprzez ich przemieszczanie w przestrzeni. Transport jest ściśle powiązany z pozostałymi działami gospodarki. Jego rozwój warunkuje ich rozwój i odwrotnie - gorszy rozwój gospodarki lub transportu wiąże się z pogorszeniem sytuacji odpowiednio w transporcie i gospodarce. W połączeniu z logistyką oraz spedycją, transport wchodzi w skład branży TSL (transport-spedycja-logistyka). Planowanie transportu wiąże z wielowymiarową oceną warunków towarzyszących problemom transportowym. W tym celu został zastosowany model rozmyty uwzględniający niepełną i nieprecyzyjną ocenę stanu transportowego.
PL
Możliwość wykorzystania robotów mobilnych w przemyśle jest w dużej mierze zależna od zastosowania efektywnych systemów sterowania. Powinny one pozwalać na autonomiczne, bezpieczne i szybkie osiąganie planowanych punktów drogi. Jednym z podstawowych problemów jest wybór odpowiednich algorytmów doboru i optymalizacji ścieżki ruchu. Ich zadaniem jest bieżące wyliczanie przebiegu drogi, omijającej przeszkody, optymalnie prowadzącej robota do postawionego, często zmieniającego się celu. Istotnym problemem w wyznaczaniu ścieżki robota mobilnego jest złożoność optymalizacji. W podejściu globalnym istnieje możliwość optymalizacji całej ścieżki, jednak wymagana jest znajomość wszystkich przeszkód przed przystąpieniem do obliczeń. Uniemożliwia to bieżące reagowanie na ich zmiany. Wadą jest także wymagana duża moc obliczeniowa. Podejście lokalne pozwala na dynamiczne reagowanie na zmieniające się przeszkody i cele. Wyznaczanie drogi można zawęzić do ograniczonego obszaru wokół robota, co znacznie zmniejsza wymagania w zakresie przetwarzania danych. Wadą jednak jest brak możliwości globalnej optymalizacji. W artykule przedstawiono wyniki badań symulacyjnych metody lokalnego planowania ścieżki robota w oparciu o wyliczanie pól potencjałowych. Opracowano uniwersalny algorytm wykorzystujący zmodyfikowaną metodę pól potencjałowych oraz zbudowano aplikację pozwalającą na wykonywanie badań symulacyjnych w oparciu o mapy otoczenia. Przy wykorzystaniu opracowanej aplikacji przeprowadzono badania symulacyjne zachowania się robotów mobilnych sterowanych różnymi algorytmami oraz poruszających się w różnych środowiskach.
EN
The possibility of using mobile robots in industry is largely dependent on the application of the efficient control systems. They should be able to achieve planned road points autonomously, safety and fast. Selection of suitable algorithms for route calculation and optimization is one of the main problems. Their task is current calculation of the road, avoiding obstacles, making the leading robot achieve often changing goal. Complexity of the optimization of a mobile robot path is one of the main problems. There are two main approaches in the mobile robot path calculation: the global and local one. In the global approach it is possible to optimize the entire path. However, it requires knowledge of all the obstacles before starting calculation, so a dynamic response to changes is impossible. The disadvantage is also required significant computing power. The local approach allow for dynamic response to changing obstacles and goals. Searching of the path can be narrowed to a limited area around robot what greatly reduce the requirements with regard to the processing of data. However, disadvantage is the inability of global optimization. The article presents the results of research on new modified potential filed method development. On the base of it a universal control algorithm has been prepared. It was used for simulation based testing of the algorithm operation in various difficult conditions like local minimum.
19
Content available Swarm intelligence approach to safe ship control
EN
This paper presents an application of the Ant Colony Optimization (ACO) technique in a safe ship control system. The method developed solves the problem of path planning and collision avoidance of a ship in the open sea as well as in restricted waters. The structure of the developed safe ship control system is introduced, followed by a presentation of the applied algorithm. Results showing the problem-solving capability of the system are also included. The aim of the system developed is to increase automation of a safe ship control process. It is possible to apply the proposed method in Unmanned Surface Vehicles (USVs) control system, what will contribute to the enhancement of their autonomy.
EN
A mobile flexible manipulator is developed in order to achieve high performance requirements such as high-speed operation, increased high payload to mass ratio, less weight, and safer operation due to reduced inertia. Hence, this paper presents a method for finding the Maximum Allowable Dynamic Load (MADL) of geometrically nonlinear flexible link mobile manipulators. The full dynamic model of a wheeled mobile base and the mounted flexible manipulator is considered with respect to dynamics of non-holonomic constraint in environment including an obstacle. In dynamical analysis, an efficient model is employed to describe the treatment of a flexible structure in which both the geometric elastic nonlinearity and the foreshortening effects are considered. Then, a path planning algorithm is developed to find the maximum payload that the optimal strategy is based on the indirect solution to the open-loop optimal control problem. In order to verify the effectiveness of the presented algorithm, several simulation studies are carried out for finding the optimal path between two points in the presence of obstacles. The results clearly show the effect of flexibility and the proposed approach on mobile flexible manipulators.
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