Dynamic gridmaps: comparing building techniques

Mobile robots need to represent obstacles in their surroundings, even moving ones, to make right movement decisions. For higher autonomy the robot should automatically build such representation from its sensory input. This paper compares the dynamic character of several gridmap building techniques: probabilistic, fuzzy, theory of evidence and histogramic. Two criteria are defined to rank such dynamism in the representation: time to show a new obstacle and time to show a new hole. The update rules for first three such techniques hold associative property which confers them static character, inconvenient for dynamic environments. Major contribution of this paper is the introduction of two new approaches are presented to improve the perception of mobile obstacles: one uses a differential equation to update the map and another uses majority voting in a limited memory per cell. Their dynamisms are also evaluated and the results presented

Dynamic gridmaps: comparing building techniques

P. 5-22Mobile robots need to represent obstacles in their surroundings, even moving ones, to make right movement decisions. For higher autonomy the robot should automatically build such representation from its sensory input. This paper compares the dynamic character of several gridmap building techniques: probabilistic, fuzzy, theory of evidence and histogramic. Two criteria are defined to rank such dynamism in the representation: time to show a new obstacle and time to show a new hole. The update rules for first three such techniques hold associative property which confers them static character, inconvenient for dynamic environments. Major contribution of this paper is the introduction of two new approaches are presented to improve the perception of mobile obstacles: one uses a differential equation to update the map and another uses majority voting in a limited memory per cell. Their dynamisms are also evaluated and the results presentedS

Development of a ground robot for indoor SLAM using Low‐Cost LiDAR and remote LabVIEW HMI

The simultaneous localization and mapping problem (SLAM) is crucial to autonomous navigation and robot mapping. The main purpose of this thesis is to develop a ground robot that implements SLAM to test the performance of the low‐cost RPLiDAR A1M8 by DFRobot. The HectorSLAM package, available in ROS was used with a Raspberry Pi to implement SLAM and build maps. These maps are sent to a remote desktop via TCP/IP communication to be displayed on a LabVIEW HMI where the user can also control robot. The LabVIEW HMI and the project in its entirety is intended to be as easy to use as possible to the layman, with many processes being automated to make this possible. The quality of the maps created by HectorSLAM and the RPLiDAR were evaluated both qualitatively and quanitatively to determine how useful the low‐cost LiDAR can be for this application. It is hoped that the apparatus developed in this project will be used with drones in the future for 3D mapping

A probabilistic framework for believes fusion

This paper presents a methodology to achieve the fusion of believes associated to logical propositions . The belief concept is introduced within the framework of logic and probability theory and the problem dealt with is the following : if different experts give their advice about a membership property, how to fuse those believes? This fusion is detailed in three cases : either the experts speak about the same thing or they speak about different things in a given set, and more generally some experts speaks about the same thing while the others characterize different objects . The proposed fusion methodology is illustrated by and example concerning environment building from ultrasonic range scans .Cet article présente un ensemble de méthodes permettant de fusionner des croyances dans des hypothèses représentées par des propositions. La notion de croyance est introduite dans le cadre de la logique des propositions et du calcul des probabilités. Le problème à traiter est le suivant : si différents experts fournissent à un système plusieurs croyances à propos d'une hypothèse ensembliste, comment ce système peut-il fusionner ces croyances? Cette fusion est envisagée dans trois cas de figure : soit les experts caractérisent le même élément, soit ils caractérisent des éléments différents appartenant à un même ensemble, soit certains caractérisent le même élément et d'autres des éléments différents. La méthodologie développée est illustrée sur un exemple de fusion multi-capteurs pour des télémètres à ultrasons utilisés en robotique mobile

Probabilistic Estimation of Incomplete Map Using Gaussian Process

학위논문 (석사)-- 서울대학교 대학원 : 전기공학부, 2013. 8. 오성회.Gaussian process is a powerful probabilistic estimation tool which is used widely in engineering fields such as Computer vision, Robotics and sensor networks, etc. This thesis implemented an estimation algorithm of the total map with sparse sensing data using Gaussian Process. In the implemented algorithm, two kinds of kernel functions are applied to the spatial Gaussian Process modelsquared exponential kernel and neural network kernel. The performance of the proposed algorithm was verified by the experiments with a simple mobile sensor network. To construct a simple mobile sensor network based on ROS (Robot Operating System) platform, a two wheeled mobile robot (Pioneer3DX) and a two dimensional laser scanner (SICKlms200) are used.1 Introduction 1 1.1 Mobile Sensor Network 1.2 Simulataneous Localization and Mapping (SLAM) 1.3 Occupancy Grid Map 2 Related Work 2.1 Mapping 2.2 Sensing and Locating 2.3 Probabilistic Solution for Mapping Problem 3 Gaussian Process (GP) 3.1 Weight-space View 3.1.1 The Standard Linear Model 3.1.2 Projections of Inputs into Feature Space 3.2 Function-space View 3.2.1 Prediction with Noise-free Observations 3.2.2 Prediction using Noisy Observations 3.3 Varying Hyperparameters 4 GP Applied to Mapping Problem 4.1 Overview of Contextual Mapping 4.2 Training Hyperparameters 5 Experimental results 5.1 The Mapping Problem in a Real Indoor Environment 5.2 GP Estimation for Single Frame of Laser Scanner 5.2.1 The given Training Data 5.2.2 Selecting a Kernel Function 5.2.3 Optimizing Hyper-parameters 5.2.4 Estimation 5.3 The Estimation Problem in a Simulated Environment 6 Conclusion 6.1 Contribution of GP for Mapping Problem 6.2 Future WorksMaste

Towards topological mapping with vision-based simultaneous localization and map building

Although the theory of Simultaneous Localization and Map Building (SLAM) is well developed, there are many challenges to overcome when incorporating vision sensors into SLAM systems. Visual sensors have different properties when compared to range finding sensors and therefore require different considerations. Existing vision-based SLAM algorithms extract point landmarks, which are required for SLAM algorithms such as the Kalman filter. Under this restriction, the types of image features that can be used are limited and the full advantages of vision not realized. This thesis examines the theoretical formulation of the SLAM problem and the characteristics of visual information in the SLAM domain. It also examines different representations of uncertainty, features and environments. It identifies the necessity to develop a suitable framework for vision-based SLAM systems and proposes a framework called VisionSLAM, which utilizes an appearance-based landmark representation and topological map structure to model metric relations between landmarks. A set of Haar feature filters are used to extract image structure statistics, which are robust against illumination changes, have good uniqueness property and can be computed in real time. The algorithm is able to resolve and correct false data associations and is robust against random correlation resulting from perceptual aliasing. The algorithm has been tested extensively in a natural outdoor environment

Biologically inspired computational structures and processes for autonomous agents and robots

Recent years have seen a proliferation of intelligent agent applications: from robots for space exploration to software agents for information filtering and electronic commerce on the Internet. Although the scope of these agent applications have blossomed tremendously since the advent of compact, affordable computing (and the recent emergence of the World Wide Web), the design of such agents for specific applications remains a daunting engineering problem;Rather than approach the design of artificial agents from a purely engineering standpoint, this dissertation views animals as biological agents, and considers artificial analogs of biological structures and processes in the design of effective agent behaviors. In particular, it explores behaviors generated by artificial neural structures appropriately shaped by the processes of evolution and spatial learning;The first part of this dissertation deals with the evolution of artificial neural controllers for a box-pushing robot task. We show that evolution discovers high fitness structures using little domain-specific knowledge, even in feedback-impoverished environments. Through a careful analysis of the evolved designs we also show how evolution exploits the environmental constraints and properties to produce designs of superior adaptive value. By modifying the task constraints in controlled ways, we also show the ability of evolution to quickly adapt to these changes and exploit them to obtain significant performance gains. We also use evolution to design the sensory systems of the box-pushing robots, particularly the number, placement, and ranges of their sensors. We find that evolution automatically discards unnecessary sensors retaining only the ones that appear to significantly affect the performance of the robot. This optimization of design across multiple dimensions (performance, number of sensors, size of neural controller, etc.) is implicitly achieved by the evolutionary algorithm without any external pressure (e.g., penalty on the use of more sensors or neurocontroller units). When used in the design of robots with limited battery capacities , evolution produces energy-efficient robot designs that use minimal numbers of components and yet perform reasonably well. The performance as well as the complexity of robot designs increase when the robots have access to a spatial learning mechanism that allows them to learn, remember, and navigate to power sources in the environment;The second part of this dissertation develops a computational characterization of the hippocampal formation which is known to play a significant role in animal spatial learning. The model is based on neuroscientific and behavioral data, and learns place maps based on interactions of sensory and dead-reckoning information streams. Using an estimation mechanism known as Kalman filtering, the model explicitly deals with uncertainties in the two information streams, allowing the robot to effectively learn and localize even in the presence sensing and motion errors. Additionally, the model has mechanisms to handle perceptual aliasing problems (where multiple places in the environment appear sensorily identical), incrementally learn and integrate local place maps, and learn and remember multiple goal locations in the environment. We show a number of properties of this spatial learning model including computational replication of several behavioral experiments performed with rodents. Not only does this model make significant contributions to robot localization, but also offers a number of predictions and suggestions that can be validated (or refuted) through systematic neurobiological and behavioral experiments with animals

Feature relative navigation for automous underwater vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1997.Includes bibliographical references (leaves 184-196).by Andrew Arnold Bennett.Ph.D