Neural Sensor Fusion for Spatial Visualization on a Mobile Robot

An ARTMAP neural network is used to integrate visual information and ultrasonic sensory information on a B 14 mobile robot. Training samples for the neural network are acquired without human intervention. Sensory snapshots are retrospectively associated with the distance to the wall, provided by on~ board odomctry as the robot travels in a straight line. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. The neural network effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.Office of Naval Research and Naval Research Laboratory (00014-96-1-0772, 00014-95-1-0409, 00014-95-0657

Mobile Robot Sensor Fusion with Fuzzy ARTMAP

The raw sensory input available to a mobile robot suffers from a variety of shortcomings. Sensor fusion can yield a percept more veridical than is available from any single sensor input. In this project, the fuzzy ARTMAP neural network is used to fuse sonar and visual sonar on a B14 mobile robot. The neural network learns to associate specific sensory inputs with a corresponding distance metric. Once trained, the network yields predictions of range to obstacles that are more accurate than those provided by either sensor type alone. This improvement in accuracy holds across all distances and angles of approach tested.Defense Advanced Research Projects Agency, Office of Naval Research, Navy Research Laboratory (ONR-00014-96-1-0772, ONR-00014-95-1-0409, ONR-00014-95-0657

Perception for mobile robot navigation: A survey of the state of the art

In order for mobile robots to navigate safely in unmapped and dynamic environments they must perceive their environment and decide on actions based on those perceptions. There are many different sensing modalities that can be used for mobile robot perception; the two most popular are ultrasonic sonar sensors and vision sensors. This paper examines the state-of-the-art in sensory-based mobile robot navigation. The first issue in mobile robot navigation is safety. This paper summarizes several competing sonar-based obstacle avoidance techniques and compares them. Another issue in mobile robot navigation is determining the robot's position and orientation (sometimes called the robot's pose) in the environment. This paper examines several different classes of vision-based approaches to pose determination. One class of approaches uses detailed, a prior models of the robot's environment. Another class of approaches triangulates using fixed, artificial landmarks. A third class of approaches builds maps using natural landmarks. Example implementations from each of these three classes are described and compared. Finally, the paper presents a completely implemented mobile robot system that integrates sonar-based obstacle avoidance with vision-based pose determination to perform a simple task

Design and data analysis for a belt-for-blind for visual impaired people

This research designs a new walking support system for the blind people in order to navigate without any assistance from others or using any guide cane. With the help of this device, a user can move independently and able to walk freely almost like a normal person. In this research, a belt for blind wearable around the waist is equipped with four ultrasonic sensors and one sharp infrared sensor. A mathematical model has been developed based on the specifications of the ultrasonic sensors to identify optimum orientation of the sensors for detecting stairs and holes. These sensors are connected to a microcontroller along with a laptop so that we can get sufficient data for analysing terrain on the walkway of the blind. Based on the analyses of the acquired data, we have developed an algorithm capable of classifying various types of obstacles. The developed belt for blind device is superior in terms of less weight less, able to detect stair and hole, low cost, less power consumption, adjustable, less training and availability of actuation systems. It was tested and implemented successfully to address all those issues

The design and intelligent control of an autonomous mobile robot

This thesis presents an investigation into the problems of exploration, map building and collision free navigation for intelligent autonomous mobile robots. The project began with an extensive review of currently available literature in the field of mobile robot research, which included intelligent control techniques and their application. It became clear that there was scope for further development with regard to map building and exploration in new and unstructured environments. Animals have an innate propensity to exhibit such abilities, and so the analogous use of artificial neural networks instead of actual neural systems was examined for use as a method of robot mapping. A simulated behaviour based mobile robot was used in conjunction with a growing cell structure neural network to map out new environments. When using the direct application of this algorithm, topological irregularities were observed to be the direct result of correlations within the input data stream. A modification to this basic system was shown to correct the problem, but further developments would be required to produce a generic solution. The mapping algorithms gained through this approach, although more similar to biological systems, are computationally inefficient in comparison to the methods which were subsequently developed. A novel mapping method was proposed based on the robot creating new location vectors, or nodes, when it exceeded a distance threshold from its mapped area. Network parameters were developed to monitor the state of growth of the network and aid the robot search process. In simulation, the combination of the novel mapping and search process were shown to be able to construct maps which could be subsequently used for collision free navigation. To develop greater insights into the control problem and to validate the simulation work the control structures were ported to a prototype mobile robot. The mobile robot was of circular construction, with a synchro-drive wheel configuration, and was equipped with eight ultrasonic distance sensors and an odometric positioning system. It was self-sufficient, incorporating all its power and computational resources. The experiments observed the effects of odometric drift and demonstrated methods of re-correction which were shown to be effective. Both the novel mapping method, and a new algorithm based on an exhaustive mesh search, were shown to be able to explore different environments and subsequently achieve collision free navigation. This was shown in all cases by monitoring the estimates in the positional error which remained within fixed bounds

Control of Real Mobile Robot Using Artificial Intelligence Technique

An eventual objective of mobile robotics research is to bestow the robot with high cerebral skill, of which navigation in an unfamiliar environment can be succeeded by using on‐line sensory information, which is essentially starved of humanoid intermediation. This research emphases on mechanical design of real mobile robot, its kinematic & dynamic model analysis and selection of AI technique based on perception, cognition, sensor fusion, path scheduling and analysis, which has to be implemented in robot for achieving integration of different preliminary robotic behaviors (e.g. obstacle avoidance, wall and edge following, escaping dead end and target seeking). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimization problem and thus can be analyzed and solved using AI techniques. The optimization of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A set of linguistic fuzzy rules are developed to implement expert knowledge under various situations. Both of Mamdani and Takagi-Sugeno fuzzy model are employed in control algorithm for experimental purpose. Neural network has also been used to enhance and optimize the outcome of controller, e.g. by introducing a learning ability. The cohesive framework combining both fuzzy inference system and neural network enabled mobile robot to generate reasonable trajectories towards the target. An authenticity checking has been done by performing simulation as well as experimental results which showed that the mobile robot is capable of avoiding stationary obstacles, escaping traps, and reaching the goal efficiently

State of the art review on walking support system for visually impaired people

The technology for terrain detection and walking support system for blind people has rapidly been improved the last couple of decades but to assist visually impaired people may have started long ago. Currently, a variety of portable or wearable navigation system is available in the market to help the blind for navigating their way in his local or remote area. The focused category in this work can be subgroups as electronic travel aids (ETAs), electronic orientation aids (EOAs) and position locator devices (PLDs). However, we will focus mainly on electronic travel aids (ETAs). This paper presents a comparative survey among the various portable or wearable walking support systems as well as informative description (a subcategory of ETAs or early stages of ETAs) with its working principal advantages and disadvantages so that the researchers can easily get the current stage of assisting blind technology along with the requirement for optimising the design of walking support system for its users

Sensor-based Collision Avoidance System for the Walking Machine ALDURO

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

Flat surface reconstruction using sonar

Journal ArticleA technique is given for the recovery of planar surfaces using two beam-spread sonar readings. If a single, planar surface gave rise to the two readings, then the method recovers the surface quite accurately. Simulation and experiment demonstrate the effectiveness of the technique and recommend its use in practice