A crash course learning: an automated approach to simulation-driven LiDAR-based training of neural networks for obstacle avoidance in mobile robotics (original) (raw)
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Turkish Journal of Electrical Engineering & Computer Sciences, 2017
Mobile robot navigation and obstacle avoidance in dynamic and unknown environments is one of the most challenging problems in the eld of robotics. Considering that a robot must be able to interact with the surrounding environment and respond to it in real time, and given the limited sensing range, inaccurate data, and noisy sensor readings, this problem becomes even more acute. In this paper, we attempt to develop a neural network approach equipped with statistical dimension reduction techniques to perform exact and fast robot navigation, as well as obstacle avoidance in such a manner. In order to increase the speed and precision of the network learning and reduce the noise, kernel principal component analysis is applied to the training patterns of the network. The proposed method uses two feedforward neural networks based on function approximation with a backpropagation learning algorithm. Two different data sets are used for training the networks. In order to visualize the robot environment, 180 ◦ laser range sensor (SICK) readings are employed. The method is tested on real-world data and experimental results are included to verify the effectiveness of the proposed method.
Application of Neural Networks in Perception System Management for an Indoor Mobile Robot
— In the mobile robotics field, localization and map building are very important tasks that a mobile robot must perform for a safe navigation. The present paper presents a multi-layer perceptron (MLP) neural classifier of obstacles with coded outputs. This means that the number of the network outputs is lower than the number of the recognizable patterns. In this way, the neural network is less cumbersome; this will make easier the network parametrizing and resizing to be applied to other type of environment. The developed classifier ensures the discrimination of all possible patterns using a little number of elements in the training set. Subsequently the problem of training slowness is avoided.
Mapless LiDAR Navigation Control of Wheeled Mobile Robots Based on Deep Imitation Learning
IEEE Access, 2021
This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sens...
An artificial neural network structure able to obstacle avoidance behavior used in mobile robots
IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02, 2002
This article presents an artificial neural network (ANN) structure applied to control a mobile robot movement in dynamically changing environments (environments wirh mobile obstacles), The proposed structure is a backward neural one. So, ir is based on past andfihrre positions, and on a optimal pre-established parh. The past pasirions provide rhe ANN with memory of the mobile robot previous positions. On the other hand, rhe future positions provide rhe ANN with a goal, i.e., where the robot shouldgo. Basedon this information, the robot da nor lose ifs goal, even (f if has to avoid an obstacle. The results show the eflciency ofthe ANXin aform ofsimulations
International Journal of Computer and Electrical Engineering, 2014
In this paper, design of an intelligent autonomous vehicle is presented that can navigate in noisy and unknown environments without hitting the obstacles in its way. The vehicle is made intelligent with the help of two multilayer feed forward neural network controllers namely 'Hurdle Avoidance Controller' and 'Goal Reaching Controller' with back error propagation as training algorithm. Hurdle avoidance controller ensures collision free motion of mobile robot while goal reaching controller helps the mobile robot in reaching the destination. Both these controllers are trained offline with the data obtained during experimental run of the robot and implemented with low cost AT89C52 microcontrollers. The computational burden on microcontrollers is reduced by using piecewise linearly approximated version of tangent-sigmoid activation function of neurons. The vehicle with the proposed controllers is tested in outdoor complex environments and is found to reach the set targets successfully. I. INTRODUCTION Navigation is the ability of a mobile robot to reach the set targets by avoiding obstacles in its way. Thus essential behaviors for robot navigation are obstacle avoidance and goal reaching [1], [2]. Conventional control techniques can be used to build controllers for these behaviors; however, the environment uncertainty imposes a serious problem in developing the complete mathematical model of the system resulting in limited usability of these controllers. Thus some kind of intelligent controllers are required that can cope with the changing environment conditions. Amongst the various artificial intelligence techniques available in literature, neural networks offer promising solution to robot navigation problem because of their ability to learn complex non linear relationships between input sensor values and output control variables. This ability of neural networks has attracted many researchers across the globe in developing neural network based controllers for reactive navigation of mobile robots in indoor as well as outdoor environments. In [3], a collision free path between source and destination is constructed based on
Autonomous bot with ML-based reactive navigation for indoor environment
arXiv (Cornell University), 2021
Local or reactive navigation is essential for autonomous mobile robots which operate in an indoor environment. Techniques such as SLAM, computer vision require significant computational power which increases cost. Similarly, using rudimentary methods makes the robot susceptible to inconsistent behavior. This paper aims to develop a robot that balances cost and accuracy by using machine learning to predict the best obstacle avoidance move based on distance inputs from four ultrasonic sensors that are strategically mounted on the front, front-left, front-right, and back of the robot. The underlying hardware consists of an Arduino Uno and a Raspberry Pi 3B. The machine learning model is first trained on the data collected by the robot. Then the Arduino continuously polls the sensors and calculates the distance values, and in case of critical need for avoidance, a suitable maneuver is made by the Arduino. In other scenarios, sensor data is sent to the Raspberry Pi using a USB connection and the machine learning model generates the best move for navigation, which is sent to the Arduino for driving motors accordingly. The system is mounted on a 2-WD robot chassis and tested in a cluttered indoor setting with most impressive results.
Autonomous Obstacle Avoidance Vehicle using LIDAR and an Embedded System
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Self-driven vehicles have many type of algorithms which simulate the driving behaviour in the real world majorly in autonomous miniature vehicles. In the last decade, several approaches have been proposed for obstacle avoidance in self-driving vehicles. In this, our team is implementing a vehicle which is capable of navigating in an unknown environment and detecting obstacle by using Raspberry Pi 3+ and a LIDAR module avoiding Computer Vision (CV) techniques. The further enhancement is being done by implementing a mapping feature to generate an image of environment during navigation.
Computational Intelligence in Robotics …, 1997
We have recently shown that a neural network model of classical and operant conditioning can be trained to control the movements of a wheeled mobile robot. The neural network learns to avoid obstacles as the robot moves around without supervision in a cluttered environment. The neural network does not require any knowledge about the quality or configuration of the sensors. In this article we report results using our neural network with the real mobile robot Khepera.
Advances in Learning for Intelligent Mobile Robots
Intelligent mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths to accomplish a variety of tasks. Such machines have many potential useful applications in medicine, defense, industry and even the home so that the design of such machines is a challenge with great potential rewards. Even though intelligent systems may have symbiotic closure that permits them to make a decision or take an action without external inputs, sensors such as vision permit sensing of the environment and permit precise adaptation to changes. Sensing and adaptation define a reactive system. However, in many applications some form of learning is also desirable or perhaps even required. A further level of intelligence called understanding may involve not only sensing, adaptation and learning but also creative, perceptual solutions involving models of not only the eyes and brain but also the mind. The purpose of this paper is to present a discussion of recent technical advances in learning for intelligent mobile robots with examples of adaptive, creative and perceptual learning. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots that could lead to important beneficial applications.