Multi-Robot Localization System using an Array of LEDs and LDR Sensors
15 Apr 2020-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 176, Iss: 10, pp 9-12
TL;DR: A new positioning system for indoor multi-robot localization by using an array of Light Emitting Diodes distributed uniformly in the environment to reduce the time of the localization process by controlling the lights of the LED array.
Abstract: a new positioning system for indoor multi-robot localization is proposed. This system solves the problem of localization by using an array of Light Emitting Diodes (LEDs) distributed uniformly in the environment. The localization is achieved by collecting the information from a group of Light Dependent Resistor (LDR) sensors with which the robot is equipped. The binary search algorithm is used to reduce the time of the localization process by controlling the lights of the LED array. The minimum bounded circle algorithm is used to draw a virtual circle from the information collected by the LDR sensors and the center of this circle represents the robot’s location. This algorithm can be implemented in a multi-robot system when the main control unit can distinguish among the LDR sensors’ information. In the case of unknown information, the K-means Clustering algorithm is used to separate this information into clusters. Each cluster can be used to estimate the location of one robot. The suggested system is simulated and practically implemented in an environment with (32*32) arrays of LEDs. The simulation and experimental results of this system show good performance in the localization process.
Citations
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12 Jun 2020
TL;DR: This paper demonstrates a new algorithm in the path planning field for indoor robot navigation, focusing on finding the shortest and most safe path from the robot source position to its target, using the shortest distance orientation algorithm.
Abstract: This paper demonstrates a new algorithm in the path planning field for indoor robot navigation, focusing on finding the shortest and most safe path from the robot source position to its target. Robot localization matters are solved by using an IR transmitter fixed to the robot and a low-cost IR sensor array that is regularly distributed in the environment. The robot location depends on a group of IR receiver sensors that sense the IR transmitter signal where the location of each IR receiver is dependent on it is the location in the IR receivers array. The robot initial position is computed by using the modified binary search algorithm to scan the IR receivers array. A path planning problem is solved by using a new algorithm called the shortest distance orientation algorithm. A chain of the IR receivers within the sensing range of the virtual path is activated to be scan through the robot moves to the target. This process helps in reducing the localization time when the robot moves toward the target. At each step of the robot movement, it is direction is corrected dependent on the angle between its orientation and the direction of the line between its location and the target location. The simulation results for different types of IR array environments are shown good performance for this algorithm.
5 citations
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...The low-cost sensors in many applications are used for determining the distance as a replacement for the expensive sensors such as camera and laser scanner [13-18]....
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TL;DR: In this paper, a street model that contains several lampposts on both sides of the street is presented, and three IR sensors are connected to the controller, and the controller takes the signal from the IR sensor, and then it sends the command to the lampost to turn on or off.
Abstract: In many countries, particularly, third world countries. The common issue is saving energy. That`s why smart systems considered now primary for life requirements. This work aims to solve the energy saving problem. We prepared a street model that contains several lampposts on both sides of the street; we placed three IR sensors between the lampposts alongside the street. The IR sensors are connected to the controller (in this work we used Arduino UNO). The controller takes the signal from the IR sensor, and then it sends the command to the lamppost to turn on or off. Depending on the number of cars passed, (we took a sample of a number of cars that passed on an actual street) and through formulas we calculated the power consumed by the lampposts in two cases, the first case is when the lights is always on. The second case is when the smart system applied. We also applied fuzzy logic to the system to take the intensity of the ambient light (the sun light) under consideration. The results showed that the proposed smart lighting system is efficient and reliable in saving energy. The energy saved for both (smart and fuzzy) systems was enormous.
2 citations
12 Jun 2020
TL;DR: A new algorithm for indoor robot path planning is presented using an IR transmitter fixed on the robot and a low-cost IR sensor array distributed regularly in the environment to solve the robot localization problem.
Abstract: In this paper, a new algorithm for indoor robot path planning is presented. The robot localization problem is solved by using an IR transmitter fixed on the robot and a low-cost IR sensor array distributed regularly in the environment. The robot location depends on a group of IR receiver sensors that sense the IR transmitter signal. The location of each IR receiver sensor depends on it is the location in the IR array and the robot location is estimated from the locations of the sensed IR receiver sensors. The localization process is applied using the modified binary search algorithm to scan the IR array and the robot location is computed by applying the centroid algorithm on the locations of the sensed IR receivers. A virtual path from the robot location to the target point is drawn using the tangent visible graph algorithm. A chain of the IR receivers within the sensing range of the virtual path is activated to be scan through the robot moves to the target. This process helps in reducing the computation time of the robot localization. At each step of the robot movement, it is direction is corrected dependent on the locations of the two next IR sensors in the chain of the active IR receivers. The simulation results for different types of IR array environments are shown good performance for this algorithm.
1 citations
Additional excerpts
...The low-cost sensors in many applications are used for determining the distance as a replacement for the expensive sensors such as camera and laser scanner [14-16]....
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01 Jul 2020
TL;DR: The robot navigation problem is solved by proposed two algorithms using low-cost IR receiver sensors arranged as an array, and a robot has been equipped with one IR transmitter, which provides safe movements and a good system control performance.
Abstract: A robot is a smart machine that can help people in their daily lives and keep everyone safe. the three general sequences to accomplish any robot task is mapping the environment, the localization, and the navigation (path planning with obstacle avoidance). Since the goal of the robot is to reach its target without colliding, the most important and challenging task of the mobile robot is the navigation. In this paper, the robot navigation problem is solved by proposed two algorithms using low-cost IR receiver sensors arranged as an array, and a robot has been equipped with one IR transmitter. Firstly, the shortest orientation algorithm is proposed, the robot direction is corrected at each step of movement depending on the angle calculation. secondly, an Active orientation algorithm is presented to solve the weakness in the preceding algorithm. A chain of the active sensors in the environment within the sensing range of the virtual path is activated to be scan through the robot movement. In each algorithm, the initial position of the robot is detected using the modified binary search algorithm, various stages are used to avoid obstacles through suitable equations focusing on finding the shortest and the safer path of the robot. Simulation results with multi-resolution environment explained the efficiency of the algorithms, they are compatible with the designed environment, it provides safe movements (without hitting obstacles) and a good system control performance. A Comparison table is also provided.
Cites methods from "Multi-Robot Localization System usi..."
...As an alternative to expensive sensors such as cameras and laser scanners, low cost sensors in many applications are used to determine distance [14-16]....
[...]
TL;DR: In this paper , a model for street lighting consisting of several lamp posts on both sides of the street was established, consisting of three IR sensors, processes these signals, and then sends the action to the lamp posts.
Abstract: Introduction: The energy consumed for street lighting is a major expenditure in urban environments. According to the World Bank, it constitutes up to 65% of cities' electricity costs and 10% of their overall budgets. The demand for lighting is growing significantly due to rapid urbanization, thus eating up even more energy and money - unless smarter solutions are deployed to reduce costs. Method: In this paper, a model for street lighting was established, consisting of several lamp posts on both sides of the street. The model was the exact replica of the street lighing system inside the city of Kirkuk, Iraq. The number of objects passing along the street was monitored, both during and out of rush hours. This all was taken into account in the energy consumption calculation. The controller used for this model is Arduino UNO. The Arduino receives signals from 3 IR sensors, processes these signals, and then sends the action to the lamp posts. Fuzzy logic was applied in two cases: the first one is during the daylight, the second one is during the sunrise and the sunset, to control the intensity of the light of the lamp posts. Results: Both cases showed significant results regarding the reliability, efficiency, and countability of the system in decreasing the level of energy consumption. Conclusion: The system can be applicable for smart city projects. It is efficient, cost effective and shows reliable results in saving energy.
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