Jason A. Janét

Autonomous mobile robot global self‐localization using Kohonen and region‐feature neural networks

This article presents and compares two neural network-based approaches to global selflocalization (GSL) for autonomous mobile robots using: (1) a Kohonen neural network; and (2) a region-feature neural network (RFNN). Both approaches categorize discrete regions of space (topographical nodes) in a manner similar to optical character recognition (OCR). That is, the mapped sonar data assumes the form of a character unique to that region. Hence, it is believed that an autonomous vehicle can determine which room it is in from sensory data gathered from exploration. With a robust exploration routine, the GSL solution can be time-, translation-, and rotation-invariant. The GSL solution can also become independent of the mobile robot used to collect the sensor data. This suggests that a single robot can transfer its knowledge of various learned regions to other mobile robots. The classification rate of both approaches are comparable and, thus, worthy of presentation. The observed pros and cons of both approaches are also discussed.  1997 John Wiley & Sons, Inc.

Autonomous mobile robot global motion planning and geometric beacon collection using traversability vectors

Approaches in global motion planning (GMP) and geometric beacon collection (for self-localization) using traversability vectors have been developed and implemented in both computer simulation and actual experiments on mobile robots. Both approaches are based on the same simple, modular, and multifunctional traversability vector (t-vector). Through implementation it has been found that t-vectors reduce the computational requirements to detect path obstructions, Euclidean optimal via-points, and geometric beacons, as well as to identify which features are visible to sensors. Environments can be static or dynamic and polygons are permitted to overlap (i.e., intersect or be nested). While the t-vector model does require that polygons be convex, it is a much simpler matter to decompose concave polygons into convex polygon sets than it is to require that polygons not overlap, which is required for many other GMP models. T-vectors also reduce the data size and complexity of standard V-graphs and variations thereof. This paper presents the t-vector model so that the reader can apply it to mobile robot GMP and self-localization.

Modeling of ultrasonic range sensors for localization of autonomous mobile robots

This paper presents a probabilistic model of ultrasonic range sensors using backpropagation neural networks trained on experimental data. The sensor model provides the probability of detecting mapped obstacles in the environment, given their position and orientation relative to the transducer. The detection probability can be used to compute the location of an autonomous vehicle from those obstacles that are more likely to be detected. The neural network model is more accurate than other existing approaches, since it captures the typical multilobal detection pattern of ultrasonic transducers. Since the network size is kept small, implementation of the model on a mobile robot can be efficient for real-time navigation. An example that demonstrates how the credence could be incorporated into the extended Kalman filter (EKF) and the numerical values of the final neural network weights are provided in the appendices.

The essential visibility graph: an approach to global motion planning for autonomous mobile robots

An approach to global motion planning for autonomous mobile robots has been developed on the basis of traversability vectors (t-vectors). Through the overall course of this research it was found that t-vectors provide a utility, efficiency and mathematical stability for collision detection and visibility that cannot be matched by commonly used algebraic approaches in static and dynamic environments. This paper will show that t-vectors also impact global motion planning by identifying redundancies in visibility graphs (V-graphs) and expediting their construction. The result of eliminating redundant path segments is a streamlined version of the V-graph called the essential visibility graph (EVG). This paper will also show that the EVG offers a significant reduction in data storage requirements and complexity.

Self-organizing geometric certainty maps: a compact and multifunctional approach to map building, place recognition and motion planning

In this paper we show how a self-organizing Kohonen neural network can use hyperellipsoid clustering (HEC) to build maps from actual sonar data. Since the HEC algorithm uses the Mahalanobis distance, the elongated shapes (typical of sonar data) can be learned. The Mahalanobis distance metric also gives a stochastic measurement of a data points association with a node. Hence, the HEC Kohonen can be used to build topographical maps and to recognize its own topographical cites for self-localization. The number of nodes can also be regulated in a self-organizing manner by using the Kolmogorov-Smirnov (KS) test for cluster compactness. The KS test determines whether a node should be divided (mitosis) or pruned completely. By incorporating principal component analysis, the HEC Kohonen can handle problems with several dimensions (3D, time-series, etc.). The HEC Kohonen is also multifunctional in that it accommodates compact geometric motion planning and self-referencing algorithms. It can also be used to solve a host of other pattern recognition problems.

Global self-localization for autonomous mobile robots using self-organizing Kohonen neural networks

An approach to global self-localization for autonomous mobile robots has been developed using self-organizing Kohonen neural networks. This approach categorizes discrete regions of space using mapped sonar data corrupted by noise of varied sources and ranges. Our approach is similar to optical character recognition (OCR) in that the mapped sonar data can, over time, assume the form of a character unique to that room. Hence, it is believed that an autonomous vehicle can be capable of determining which room it is in based on mapped sensory data ascertained by wandering through and exploring that room. With some pre-processing and a robust explore routine, the solution becomes time-, translation- and rotation-invariant.

Applications of control networks in distributed robotic systems

Our research centers around finding hardware and software components that enable us to mimic biological entities and colonies within the context of multi-agent systems. Specifically, we use control networks with fieldbus or wireless data links to connect distributed hardware and software modules. Although there are many implementations of multi-agent systems, most literature seems to focus on the algorithmic aspects. In this paper, we describe how LonWorks control networks, coupled with minimal custom electromechanical devices, can be used to construct multi-agent systems. Important issues include real-time system constraints, fault-tolerance, synchronization, and, of course, solving the robotic tasks at hand. We describe three proof-of-concept projects currently underway: a biped walking robot; an hexapod colony; and a complex autonomous vehicle. It is believed that attributes observed in these projects can be used to build application-specific systems for automation, space exploration, hazardous materials operations, etc.

Using control networks for distributed robotic systems

Through our work we have identified hardware and software components that enable us to mimic biological entities and colonies within the context of multi-agent systems. Specifically, we use control networks with fieldbus or wireless data links to connect distributed hardware and software modules. Although there are many implementations of multi-agent systems, most literature seems to focus on the algorithmic aspects. We describe how (LonWorks) control networks, coupled with minimal custom electromechanical devices, can be used to construct multi-agent systems. Important issues include real-time system constraints, fault-tolerance, synchronization, and, of course, solving the robotic tasks at hand. We describe three proof-of-concept projects currently underway: a biped walking robot; a hexapod colony; and a complex autonomous vehicle. It is believed that attributes observed in these projects can be used to build application-specific systems in other areas.

Sonar windows and geometrically represented objects for mobile robot self-referencing

When navigating through an environment, a mobile robot updates its position and orientation by searching for known objects called geometric beacons. This operation of comparing predicted sonar ranges with observed ones is referred to as self-referencing. There are two fundamental steps necessary for solving the self-referencing problem: how to represent the objects in the environment and how to search for landmarks based on this representation. This paper presents a method of mobile robot self-referencing where every object in the environment can be used as a landmark beacon. First, it shows how to interpret and predict sonar range readings through regional sampling and sensor window construction. Second, it justifies geometrically representing obstacles on the basis of surface information quality, compatibility with global motion planning, and the computer storage necessary to satisfy both criteria. Finally, it justifies the use of traversability vectors and configuration-space-time based on how they can be simultaneously used to plan motion globally and facilitate the search for geometric beacons.

T-vectors make autonomous mobile robot motion planning and self-referencing more efficient

A motion planning and self-referencing approach has been developed, simulated and applied to an actual robot. Although there are several novelties to these approaches, the fact that both are based on traversability vectors (t-vectors) is one aspect of this research that is unique. Through their application it has been found that t-vectors enhance the detection of path obstructions and geometric beacons and expedite the identification of features that are visible (or hearable) to sensors in both static and dynamic environments. T-vectors also reduce the data size and complexity of standard V-graphs and variations thereof. This paper provides the t-vector models step-by-step so that the reader will be able to apply them to mobile robot motion planning and self-referencing.<<ETX>>