2,257 research outputs found

    Optimization of a Simultaneous Localization and Mapping (SLAM) System for an Autonomous Vehicle Using a 2-Dimensional Light Detection and Ranging Sensor (LiDAR) by Sensor Fusion

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    Fully autonomous vehicles must accurately estimate the extent of their environment as well as their relative location in their environment. A popular approach to organizing such information is creating a map of a given physical environment and defining a point in this map representing the vehicle’s location. Simultaneous Mapping and Localization (SLAM) is a computing algorithm that takes inputs from a Light Detection and Ranging (LiDAR) sensor to construct a map of the vehicle’s physical environment and determine its respective location in this map based on feature recognition simultaneously. Two fundamental requirements allow an accurate SLAM method: one being accurate distance measurements and the second being an accurate assessment of location. Researched are methods in which a 2D LiDAR sensor system with laser range finders, ultrasonic sensors and stereo camera vision is optimized for distance measurement accuracy, particularly a method using recurrent neural networks. Sensor fusion techniques with infrared, camera and ultrasonic sensors are implemented to investigate their effects on distance measurement accuracy. It was found that the use of a recurrent neural network for fusing data from a 2D LiDAR with laser range finders and ultrasonic sensors outperforms raw sensor data in accuracy (46.6% error reduced to 3.0% error) and precision (0.62m std. deviation reduced to 0.0015m std. deviation). These results demonstrate the effectiveness of machine learning based fusion algorithms for noise reduction, measurement accuracy improvement, and outlier measurement removal which would provide SLAM vehicles more robust performance

    Navigation and Control of Automated Guided Vehicle using Fuzzy Inference System and Neural Network Technique

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    Automatic motion planning and navigation is the primary task of an Automated Guided Vehicle (AGV) or mobile robot. All such navigation systems consist of a data collection system, a decision making system and a hardware control system. Artificial Intelligence based decision making systems have become increasingly more successful as they are capable of handling large complex calculations and have a good performance under unpredictable and imprecise environments. This research focuses on developing Fuzzy Logic and Neural Network based implementations for the navigation of an AGV by using heading angle and obstacle distances as inputs to generate the velocity and steering angle as output. The Gaussian, Triangular and Trapezoidal membership functions for the Fuzzy Inference System and the Feed forward back propagation were developed, modelled and simulated on MATLAB. The reserach presents an evaluation of the four different decision making systems and a study has been conducted to compare their performances. The hardware control for an AGV should be robust and precise. For practical implementation a prototype, that functions via DC servo motors and a gear systems, was constructed and installed on a commercial vehicle

    Sensor-based Collision Avoidance System for the Walking Machine ALDURO

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    This work presents a sensor system develop for the robot ALDURO (Antropomorphically Legged and Wheeled Duisburg Robot), in order to allow it to detect and avoid obstacles when moving in unstructured terrains. The robot is a large-scale hydraulically driven 4-legged walking-machine, developed at the Duisburg-Essen University, with 16 degrees of freedom at each leg and will be steered by an operator sitting in a cab on the robot body. The Cartesian operator instructions are processed by a control computer, which converts them into appropriate autonomous leg movements, what makes necessary that the robot automatically recognizes the obstacles (rock, trunks, holes, etc.) on its way, locates and avoids them. A system based on ultra-sound sensors was developed to carry this task on, but there are intrinsic problems with such sensors, concerning to their poor angular precision. To overcome that, a fuzzy model of the used ultra-sound sensor, based on the characteristics of the real one, was developed to include the uncertainties about the measures. A posterior fuzzy inference builds from the measured data a map of the robot’s surroundings, to be used as input to the navigation system. This whole sensor system was implemented at a test stand, where a real size leg of the robot is fully functional. The sensors are assembled in an I2C net, which uses a micro-controller as interface to the main controller (a personal computer). That enables to relieve the main controller of some data processing, which is carried by the microcontroller on. The sensor system was tested together with the fuzzy data inference, and different arrangements to the sensors and settings of the inference system were tried, in order to achieve a satisfactory result

    Design and modeling of a stair climber smart mobile robot (MSRox)

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    Dynamic gridmaps: comparing building techniques

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    Mobile robots need to represent obstacles in their surroundings, even moving ones, to make right movement decisions. For higher autonomy the robot should automatically build such representation from its sensory input. This paper compares the dynamic character of several gridmap building techniques: probabilistic, fuzzy, theory of evidence and histogramic. Two criteria are defined to rank such dynamism in the representation: time to show a new obstacle and time to show a new hole. The update rules for first three such techniques hold associative property which confers them static character, inconvenient for dynamic environments. Major contribution of this paper is the introduction of two new approaches are presented to improve the perception of mobile obstacles: one uses a differential equation to update the map and another uses majority voting in a limited memory per cell. Their dynamisms are also evaluated and the results presented

    Dynamic gridmaps: comparing building techniques

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    P. 5-22Mobile robots need to represent obstacles in their surroundings, even moving ones, to make right movement decisions. For higher autonomy the robot should automatically build such representation from its sensory input. This paper compares the dynamic character of several gridmap building techniques: probabilistic, fuzzy, theory of evidence and histogramic. Two criteria are defined to rank such dynamism in the representation: time to show a new obstacle and time to show a new hole. The update rules for first three such techniques hold associative property which confers them static character, inconvenient for dynamic environments. Major contribution of this paper is the introduction of two new approaches are presented to improve the perception of mobile obstacles: one uses a differential equation to update the map and another uses majority voting in a limited memory per cell. Their dynamisms are also evaluated and the results presentedS

    Fuzzy clustering and enumeration of target type based on sonar returns

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    Cataloged from PDF version of article.The fuzzy c-means (FCM) clustering algorithm is used in conjunction with a cluster validity criterion, to determine the number of different types of targets in a given environment, based on their sonar signatures. The class of each target and its location are also determined. The method is experimentally verified using real sonar returns from targets in indoor environments. A correct differentiation rate of 98% is achieved with average absolute valued localization errors of 0.5 cm and 0.8degrees in range and azimuth, respectively. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved

    Real-time performance-focused on localisation techniques for autonomous vehicle: a review

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