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

A Real-Time Novelty Detector for a Mobile Robot

Recognising new or unusual features of an environment is an ability which is potentially very useful to a robot. This paper demonstrates an algorithm which achieves this task by learning an internal representation of `normality' from sonar scans taken as a robot explores the environment. This model of the environment is used to evaluate the novelty of each sonar scan presented to it with relation to the model. Stimuli which have not been seen before, and therefore have more novelty, are highlighted by the filter. The filter has the ability to forget about features which have been learned, so that stimuli which are seen only rarely recover their response over time. A number of robot experiments are presented which demonstrate the operation of the filter.Comment: 8 pages, 6 figures. In Proceedings of EUREL European Advanced Robotics Systems Masterclass and Conference, 200

Accurate robot simulation through system identification

Robot simulators are useful tools for developing robot behaviours. They provide a fast and efficient means to test robot control code at the convenience of the office desk. In all but the simplest cases though, due to the complexities of the physical systems modelled in the simulator, there are considerable differences between the behaviour of the robot in the simulator and that in the real world environment. In this paper we present a novel method to create a robot simulator using real sensor data. Logged sensor data is used to construct a mathematically explicit model(in the form of a NARMAX polynomial) of the robot’s environment. The advantage of such a transparent model — in contrast to opaque modelling methods such as artificial neural networks — is that it can be analysed to characterise the modelled system, using established mathematical methods In this paper we compare the behaviour of the robot running a particular task in both the simulator and the real-world using qualitative and quantitative measures including statistical methods to investigate the faithfulness of the simulator

Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.Comment: 200

A hybrid approach to simultaneous localization and mapping in indoors environment

This thesis will present SLAM in the current literature to benefit from then it will present the investigation results for a hybrid approach used where different algorithms using laser, sonar, and camera sensors were tested and compared. The contribution of this thesis is the development of a hybrid approach for SLAM that uses different sensors and where different factors are taken into consideration such as dynamic objects, and the development of a scalable grid map model with new sensors models for real time update of the map.The thesis will show the success found, difficulties faced and limitations of the algorithms developed which were simulated and experimentally tested in an indoors environment

Appearance-based localization for mobile robots using digital zoom and visual compass

This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally

A Neural Network Strategy Applied in Autonomous Mobile Localization

In this article, a new approach to the problem of indoor navigation based on ultrasonic sensors is presented, where artificial neural networks (ANN) are used to estimate the position and orientation of a mobile robot.
This approach proposes the use of three Radial Basis Function (RBF) Networks, where environment maps from an ultrasonic sensor and maps synthetically generated are used to estimate the robot localization.
The mobile robot is mainly characterized by its real time
operation based on the Matlab/Simulink environment, where the
whole necessary tasks for an autonomous navigation are done in a hierarchical and easy reprogramming way. 
Finally, practical results of real time navigation related to robot localization in a known indoor environment are shown