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1

Effect of ship neural domain shape on safe and optimal trajectory

Lisowski J.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2023

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Vol. 17 No. 1

185--191

EN

This article presents the task of safely guiding a ship, taking into account the movement of many other marine units. An optimally neural modified algorithm for determining a safe trajectory is presented. The possible shapes of the domains assigned to other ships as traffic restrictions for the particular ship were subjected to a detailed analysis. The codes for the computer program Neuro-Constraints for generating these domains are presented. The results of the simulation tests of the algorithm for a navigational situation are presented. The safe trajectories of the ship were compared at different distances, changing the sailing conditions and ship sizes.

2

A nature inspired collision avoidance algorithm for ships

Lazarowska A.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2023

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Vol. 17 No. 2

341--346

EN

Nature inspired algorithms are regarded as a powerful tool for solving real life problems. They do not guarantee to find the globally optimal solution, but can find a suboptimal, robust solution with an acceptable computational cost. The paper introduces an approach to the development of collision avoidance algorithms for ships based on the firefly algorithm, classified to the swarm intelligence methods. Such algorithms are inspired by the swarming behaviour of animals, such as e.g. birds, fish, ants, bees, fireflies. The description of the developed algorithm is followed by the presentation of simulation results, which show, that it might be regarded as an efficient method of solving the collision avoidance problem. Such algorithm is intended for use in the Decision Support System or in the Collision Avoidance Module of the Autonomous Navigation System for Maritime Autonomous Surface Ships.

3

Obstacle avoidance method of autonomous vehicle based on fusion improved A*APF algorithm

Qian Yubin, Sun Hongtao, Feng Song

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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Vol. 71, nr 2

art. no. e144624

EN

This paper proposes an autonomous obstacle avoidance method combining improved A-star (A*) and improved artificial potential field (APF) to solve the planning and tracking problems of autonomous vehicles in a road environment. The A*APF algorithm to perform path planning tasks, and based on the longitudinal braking distance model, a dynamically changing obstacle influence range is designed. When there is no obstacle affecting the controlled vehicle, the improved A* algorithm with angle constraint combined with steering cost can quickly generate the optimal route and reduce turning points. If the controlled vehicle enters the influence domain of obstacle, the improved artificial potential field algorithm will generate lane changing paths and optimize the local optimal locations based on simulated annealing. Pondering the influence of surrounding participants, the four-mode obstacle avoidance process is established, and the corresponding safe distance condition is analyzed. A particular index is introduced to comprehensively evaluate speed, risk warning, and safe distance factors, so the proposed method is designed based on the fuzzy control theory. In the tracking task, a model predictive controller in the light of the kinematics model is devised to make the longitudinal and lateral process of lane changing meet comfort requirements, generating a feasible autonomous lane-change path. Finally, the simulation was performed in the Matlab/Simulink and Carsim combined environment. The proposed fusion path generation algorithm can overcome the shortcomings of the traditional single method and better adapt to the dynamic environment. The feasibility of the obstacle avoidance algorithm is verified in the three-lane simulation scenario to meet safety and comfort requirements.

4

Automated UAV to Survey and Monitor Ionising Radiation Levels in a Closed Environment

Anand R., Harshith Kumar M. B., Raghavan Anirudh, Maddara Roopak, Anand Prajna

Power Electronics and Drives

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2022

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Vol. 7 (42)

134--145

EN

Since tragedies caused by nuclear disasters are always a concern, it is essential that nuclear power plants be monitored on a regular basis for any irregularities in ionising radiation levels. Irrespective of leakage proof measures being deployed in the plant, ensuring the safety of these measures is necessary. Given this scenario, the present study proposes the usage of unmanned aerial vehicles (UAVs) to ensure that radiation levels in nuclear plants remain within safe limits. The UAV deployed will map the entire environment following a unique path planning algorithm and monitor the environment with an onboard radiation sensor. If any irregularities are detected, the positional coordinates are flagged, and the A* algorithm is implemented to generate the shortest path between the starting point, and the flagged coordinates, which are considered as the destination coordinates. The UAV is made to traverse the shortest path together with maintaining stability of the system while traversing.

5

A collision avoidance algorithm in Simultaneous Localization and Mapping problem for mobile platforms

Małecki Tomasz, Narkiewicz Janusz

Journal of Theoretical and Applied Mechanics

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2022

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Vol. 60 nr 2

317--328

EN

A collision avoidance algorithm applicable in simultaneous localization and mapping (SLAM) has been developed with a prospect of an on-line application for mobile platforms to search and map the operation area and avoid contact with obstacles. The algorithm, which was implemented in MATLAB software, is based on a linear discrete-time state transition model for determination of the platform position and orientation, and a ‘force’ points method for collision avoidance and definition of the next-step of platform motion. The proposed approach may be incorporated into real-time applications with limited on-board computational resources.

6

A scalable tree based path planning for a service robot

Nippun Kumaar A. A., Kochuvila Sreeja, Nagaraja S. R.

Journal of Automation Mobile Robotics and Intelligent Systems

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2022

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Vol. 16, No. 1

31--45

EN

Path planning plays a vital role in a mobile robot navi‐ gation system. It essentially generates the shortest tra‐ versable path between two given points. There are many path planning algorithms that have been proposed by re‐ searchers all over the world; however, there is very little work focussing on path planning for a service environ‐ ment. The general assumption is that either the environ‐ ment is fully known or unknown. Both cases would not be suitable for a service environment. A fully known en‐ vironment will restrict further expansion in terms of the number of navigation points and an unknown environ‐ ment would give an inefficient path. Unlike other envi‐ ronments, service environments have certain factors to be considered, like user‐friendliness, repeatability, sca‐ lability, and portability, which are very essential for a service robot. In this paper, a simple, efficient, robust, and environment‐independent path planning algorithm for an indoor mobile service robot is presented. Initially, the robot is trained to navigate to all the possible desti‐ nations sequentially with a minimal user interface, which will ensure that the robot knows partial paths in the en‐ vironment. With the trained data, the path planning al‐ gorithm maps all the logical paths between all the des‐ tinations, which helps in autonomous navigation. The al‐ gorithm is implemented and tested using a 2D simulator Player/Stage. The proposed system is tested with two dif‐ ferent service environment layouts and proved to have features like scalability, trainability, accuracy, and repe‐ atability. The algorithm is compared with various classi‐ cal path planning algorithms and the results show that the proposed path planning algorithm is on par with the other algorithms in terms of accuracy and efficient path generation.

7

Humanoid robot path planning using rapidly explored random tree and motion primitives

Szumowski Maksymilian, Zielinska Teresa

Journal of Automation Mobile Robotics and Intelligent Systems

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2021

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Vol. 15, No. 1

24--30

EN

Path planning is an essential function of the control sy‐ stem of any mobile robot. In this article the path planner for a humanoid robot is presented. The short description of an universal control framework and the Motion Ge‐ neration System is also presented. Described path plan‐ ner utilizes a limited number of motions called the Mo‐ tion Primitives. They are generated by Motion Generation System. Four different algorithms, namely the: Informed RRT, Informed RRT with random bias, and RRT with A* like heuristics were tested. For the last one the version with biased random function was also considered. All menti‐ oned algorithms were evaluated considering three diffe‐ rent scenarios. Obtained results are described and discus‐ sed.

8

FDA* : a focused single‐query grid based path planning algorithm

Boumediene Mouad, Lamine Mehennaoui Lamine, Lachouri Abderazzak

Journal of Automation Mobile Robotics and Intelligent Systems

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2021

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Vol. 15, No. 3

37--43

EN

Square grid representations of the state‐space are a commonly used tool in path planning. With applications in a variety of disciplines, including robotics, computational biology, game development, and beyond. However, in large‐scale and/or high dimensional environments the creation and manipulation of such structures become too expensive, especially in applications when an accurate representation is needed. In this paper, we present a method for reducing the cost of single‐query grid‐based path planning, by focusing the search to a smaller subset, that contains the optimal solution. This subset is represented by a hyperrectangle, the location, and dimensions of which are calculated departing from an initial feasible path found by a fast search using the RRT* algorithm. We also present an implementation of this focused discretization method called FDA*, a resolution optimal algorithm, where the A* algorithm is employed in searching the resulting graph for an optimal solution. We also demonstrate through simulation results, that the FDA* algorithm uses less memory and has a shorter run‐time compared to the classic A* and thus other graph‐based planning algorithms, and at the same time, the resulting path cost is less than that of regular RRT based algorithms.

9

Path planning for an autonomous underwater vehicle in a cluttered underwater environment based on the heat method

Sun Kaiyue, Liu Xiangyang

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 2

289--301

EN

This paper proposes a novel autonomous underwater vehicle path planning algorithm in a cluttered underwater environment based on the heat method. The algorithm calculates the isotropic and anisotropic geodesic distances by adding the direction and magnitude of the currents to the heat method, which is named the anisotropy-based heat method. Taking account of the relevant influence of the environment on the cost functions, such as currents, obstacles and turn of the vehicle, an efficient collision-free and energy-optimized path solution can be obtained. Simulation results show that the anisotropy-based heat method is able to find a good trajectory in both static and dynamic clutter fields (including uncertain obstacles and changing currents). Compared with the fast marching (FM) algorithm, the anisotropy-based heat method is not only robust, flexible, and simple to implement, but it also greatly saves time consumption and memory footprint in a time-variant environment. Finally, the evaluation criteria of paths are proposed in terms of length, arrival time, energy consumption, and smoothness.

10

Chaotic path planning for grid coverage using a modified logistic-may map

Petavratzis Eleftherios, Moysis Lazaros, Volos Christos, Nistazakis Hector, Muñoz-Pacheco Jesus Manuel, Stouboulos Ioannis

Journal of Automation Mobile Robotics and Intelligent Systems

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2020

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Vol. 14, No. 2

3--9

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.

11

Application of the ACO algorithm for UAV path planning

Konatowski Stanisław, Pawłowski Paweł

Przegląd Elektrotechniczny

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2019

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R. 95, nr 7

115--119

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.

12

Collision-free autonomous robot navigation in unknown environments utilizing PSO for path planning

Krell Evan, Sheta Alaa, Balasubramanian Arun Prassanth Ramaswamy, King Scott A.

Journal of Artificial Intelligence and Soft Computing Research

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2019

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Vol. 9, No. 4

267--282

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.

13

An offline path planning method for autonomous vehicles

Barsi Á., Nyerges Á., Potó V., Tihanyi V.

Production Engineering Archives

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2018

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Vol. 19

37--42

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.

14

An exact geometry-based algorithm for path planning

Jafarzadeh H., Fleming C. H.

International Journal of Applied Mathematics and Computer Science

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2018

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Vol. 28, no. 3

493--504

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.

15

Planowanie trasy bezzałogowego statku powietrznego podczas lotu nad terenem z przeszkodami

Basmadji F. L., Gruszecki J.

Zeszyty Naukowe Politechniki Rzeszowskiej. Mechanika

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2017

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z. 89 [295], nr 1

5--17

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.

16

A method of artificial potential fields for the determination of ship’s safe trajectory

Mielniczuk S.

Zeszyty Naukowe Akademii Morskiej w Szczecinie

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2017

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nr 51 (123)

45--49

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.

17

Mobile Robot Path Planning with Obstacle Avoidance using Particle Swarm Optimization

Chołodowicz E., Figurowski D.

Pomiary Automatyka Robotyka

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2017

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R. 21, nr 3

59--68

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.

18

Vibration optimization of a two-link flexible manipulator with optimal input torques

Pouya M., Vahidi Pashaki P.

Mechanics and Mechanical Engineering

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2017

|

Vol. 21, nr 2

253--265

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.

19

Surface-to-air missile path planning using genetic and PSO algorithms

Zandavi S. M.

Journal of Theoretical and Applied Mechanics

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2017

|

Vol. 55 nr 3

801--812

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.

20

On automatic metric radio map generation for the purpose of WiFi navigation

Bigaj P., Bartoszek D.

Journal of Automation Mobile Robotics and Intelligent Systems

|

2017

|

Vol. 11, No. 3

62--73

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.