Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

A Real-Time Unsupervised Neural Network for the Low-Level Control of a Mobile Robot in a Nonstationary Environment

This article introduces a real-time, unsupervised neural network that learns to control a two-degree-of-freedom mobile robot in a nonstationary environment. The neural controller, which is termed neural NETwork MObile Robot Controller (NETMORC), combines associative learning and Vector Associative Map (YAM) learning to generate transformations between spatial and velocity coordinates. As a result, the controller learns the wheel velocities required to reach a target at an arbitrary distance and angle. The transformations are learned during an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. Aside form being able to reach stationary or moving targets, the NETMORC structure also enables the robot to perform successfully in spite of disturbances in the enviroment, such as wheel slippage, or changes in the robot's plant, including changes in wheel radius, changes in inter-wheel distance, or changes in the internal time step of the system. Finally, the controller is extended to include a module that learns an internal odometric transformation, allowing the robot to reach targets when visual input is sporadic or unreliable.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499

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

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

An Incremental Navigation Localization Methodology for Application to Semi-Autonomous Mobile Robotic Platforms to Assist Individuals Having Severe Motor Disabilities.

In the present work, the author explores the issues surrounding the design and development of an intelligent wheelchair platform incorporating the semi-autonomous system paradigm, to meet the needs of individuals with severe motor disabilities. The author presents a discussion of the problems of navigation that must be solved before any system of this type can be instantiated, and enumerates the general design issues that must be addressed by the designers of systems of this type. This discussion includes reviews of various methodologies that have been proposed as solutions to the problems considered. Next, the author introduces a new navigation method, called Incremental Signature Recognition (ISR), for use by semi-autonomous systems in structured environments. This method is based on the recognition, recording, and tracking of environmental discontinuities: sensor reported anomalies in measured environmental parameters. The author then proposes a robust, redundant, dynamic, self-diagnosing sensing methodology for detecting and compensating for hidden failures of single sensors and sensor idiosyncrasies. This technique is optimized for the detection of spatial discontinuity anomalies. Finally, the author gives details of an effort to realize a prototype ISR based system, along with insights into the various implementation choices made