Wyniki wyszukiwania - BazTech

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Algorytm planowania trasy mobilnej platformy wykorzystywanej do dtekcji zagrożeń w sieciach WSN

Bazydło P., Lasota K., Kozakiewicz A.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2016

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nr 8-9

807--810, CD

PL

Artykuł skupia się na zagadnieniu planowania trajektorii platformy mobilnej (robota) w systemie MOTHON, przeznaczonym do wykrywania zagrożeń w sieciach WSN. Omówiono podstawowe cele systemu oraz zaprezentowano algorytm globalnego planowania trajektorii platformy wykorzystujący predefiniowaną mapę obszaru. Środowisko pracy robota uwzględnia nieprzejezdne przeszkody statyczne oraz obszary, które robot powinien omijać. Działanie prezentowanego algorytmu zostało potwierdzone poprzez przeprowadzenie symulacji w środowisku V-REP.

EN

This paper presents a path planning algorithm for a mobile platform (robot) used in MOTHON system, designed for intrusion detection in WSN networks. In this paper, the primary goals of the system have been described, together with the architecture of the platform and the global path planning algorithm. In this particular case, the robot moves in the environment, which consists of static obstacles. Moreover, areas which should be omitted are included. Global path planning algorithm for the described system has been verified on the basis of simulation using V-REP software.

2

Model–based energy efficient global path planning for a four–wheeled mobile robot

Jaroszek P., Trojnacki M.

Control and Cybernetics

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2014

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Vol. 43, no. 2

337--363

EN

This paper concerns an energy efficient global path planning algorithm for a four-wheeled mobile robot (4WMR). First, the appropriate graph search methods for robot path planning are described. The A* heuristic algorithm is chosen to find an optimal path on a 2D tile-decomposed map. Various criteria of optimization in path planning, like mobility, distance, or energy are reviewed. The adequate terrain representation is introduced. Each cell in the map includes information about ground height and type. Tire-ground interface for every terrain type is characterized by coefficients of friction and rolling resistance. The goal of the elaborated algorithm is to find an energy minimizing route for the given environment, based on the robot dynamics, its motor characteristics, and power supply constraints. The cost is introduced as a function of electrical energy consumption of each motor and other robot devices. A simulation study was performed in order to investigate the power consumption level for diverse terrain. Two 1600 m2 test maps, representing field and urban environments, were decomposed into 20x20 equal-sized square-shaped elements. Several simulation experiments have been carried out to highlight the differences between energy consumption of the classic shortest path approach, where cost function is represented as the path length, and the energy efficient planning method, where cost is related to electrical energy consumed during robot motion.

3

FPGA-Based Hybrid GA-PSO Algorithm and Its Application to Global Path Planning for Mobile Robots

Huang H.-C.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 7b

281--284

EN

This paper presents an FPGA-based (field-programmable gate array) hybrid metaheuristic GA (genetic algorithm)-PSO (particle swarm optimization) algorithm for mobile robots to find an optimal path between a starting and ending point in a grid environment. GA has been combined with PSO in evolving new solutions by applying crossover and mutation operators on solutions constructed by particles. This hybrid algorithm avoids the premature convergence and time complexity in conventional GA and PSO algorithms. The initial feasible path generated from the hybrid GAPSO planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Experimental results are conducted to show the merit of the proposed hybrid GA-PSO path planner for global path planning for mobile robots.

PL

W artykule zaprezentowano algorytm dla mobilnych robotów poszukujący optymalnej ścieżki między punktem startu i końcowym. Algorytm wykorzystuje układy FPGA i bazuje na algorytmach genetycznych i mrówkowych.