Alessandro Saffiotti

The uses of fuzzy logic in autonomous robot navigation

Abstract The development of techniques for autonomous navigation in real-world environments constitutes one of the major trends in the current research on robotics. An important problem in autonomous navigation is the need to cope with the large amount of uncertainty that is inherent of natural environments. Fuzzy logic has features that make it an adequate tool to address this problem. In this paper, we review some of the possible uses of fuzzy logic in the field of autonomous navigation. We focus on four issues: how to design robust behavior-producing modules; how to coordinate the activity of several such modules; how to use data from the sensors; and how to integrate high-level reasoning and low-level execution. For each issue, we review some of the proposals in the literature, and discuss the pros and cons of fuzzy logic solutions.

A multivalued logic approach to integrating planning and control

Abstract elligent agents embedded in a dynamic, uncertain environment should incorporate capabilities for both planned and reactive behavior. Many current solutions to this dual need focus on one aspect, and treat the other one as secondary. We propose an approach for integrating planning and control based on behavior schemas , which link physical movements to abstract action descriptions. Behavior schemas describe behaviors of an agent, expressed as trajectories of control actions in an environment, and goals can be defined as predicates on these trajectories. Goals and behaviors can be combined to produce conjoint goals and complex controls. The ability of multivalued logics to represent graded preferences allows us to formulate tradeoffs in the combination. Two composition theorems relate complex controls to complex goals, and provide the key to using standard knowledge-based deliberation techniques to generate complex controllers. We report experiments in planning and execution on a mobile robot platform, Flakey.

An introduction to the anchoring problem

Anchoring is the problem of connecting, inside an artificial system, symbols and sensor data that refer to the same physical objects in the external world. This problem needs to be solved in any robotic system that incorporates a symbolic component. However, it is only recently that the anchoring problem has started to be addressed as a problem per se, and a few general solutions have begun to appear in the literature. This paper introduces the special issue on perceptual anchoringof the Robotics and Autonomous Systemsjournal. Our goal is to provide a general overview of the anchoring problem, and highlight some of its subtle points.

Multi-hierarchical semantic maps for mobile robotics

The success of mobile robots, and particularly of those interfacing with humans in daily environments (e.g., assistant robots), relies on the ability to manipulate information beyond simple spatial relations. We are interested in semantic information, which gives meaning to spatial information like images or geometric maps. We present a multi-hierarchical approach to enable a mobile robot to acquire semantic information from its sensors, and to use it for navigation tasks. In our approach, the link between spatial and semantic information is established via anchoring. We show experiments on a real mobile robot that demonstrate its ability to use and infer new semantic information from its environment, improving its operation.

The Saphira architecture: a design for autonomy

Mobile robots, if they are to perform useful tasks and become accepted in open environments, must be fully autonomous. Autonomy has many different aspects ; here the focus is on three central ones: the ability to attend to another agent, to take advice about the environment, and to carry out assigned tasks. All three involve complex sensing and planning operations on the part of the robot, including the use of visual tracking of humans, coordination of motor controls, and planning. It is shown how these capabilities are integrated in the Saphira architecture, using the concepts of coordination of behaviour, coherence of modelling, and communication with other agents.

Robot task planning using semantic maps

Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, such as labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation and localization), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowledge, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrates hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains. We show several experiments that demonstrate the effectiveness of our solutions in a domain involving robot navigation in a domestic environment.

Fusion: General concepts and characteristics

The problem of combining pieces of information issued from several sources can be encountered in various fields of application. This paper aims at presenting the different aspects of information fusion in different domains, such as databases, regulations, preferences, sensor fusion, etc., at a quite general level. We first present different types of information encountered in fusion problems, and different aims of the fusion process. Then we focus on representation issues which are relevant when discussing fusion problems. An important issue is then addressed, the handling of conflicting information. We briefly review different domains where fusion is involved, and describe how the fusion problems are stated in each domain. Since the term fusion can have different, more or less broad, meanings, we specify later some terminology with respect to related problems, that might be included in a broad meaning of fusion. Finally we briefly discuss the difficult aspects of validation and evaluation. © 2001 John Wiley & Sons, Inc.

Blending reactivity and goal-directedness in a fuzzy controller

Controlling the movement of an autonomous mobile robot requires the ability to pursue strategic goals in a highly reactive way. The authors describe a fuzzy controller for such a mobile robot that can take abstract goals into consideration. Through the use of fuzzy logic, reactive behavior, e.g., avoiding obstacles on the way, and goal-oriented behavior, e.g., trying to reach a given location, are smoothly blended into one sequence of control actions. The technique proposed has been implemented in the SRI mobile robot Flakey, resulting in extremely smooth and reliable movement.<<ETX>>

PEIS ecologies: ambient intelligence meets autonomous robotics

A common vision in the field of autonomous robotics is to create a skilled robot companion that is able to live in our homes and perform physical tasks to help us in our everyday life. Another vision, coming from the field of ambient intelligence, is to create a network of intelligent home devices that provide us with information, communication, and entertainment. We propose to combine these two visions into the new concept of an ecology of networked Physically Embedded Intelligent Systems (PEIS). In this paper, we define this concept, and illustrate it by describing an experimental system that involves real robotic devices.

Fuzzy Logic Techniques for Autonomous Vehicle Navigation

Tutorials: A. Saffiotti: Fuzzy Logic in Autonomous Navigation.- D. Driankov: A Reminder of Fuzzy Logic.- Design of Individual Behaviors: A. Ollero, J. Ferruz, O. Sanchez, G. Heredia: Mobile Robot Path Tracking and Visual Target Tracking Using Fuzzy Logic.- R.R. Murphy: Fuzzy Logic for Fusion of Tactical Influences on Vehicle Speed Control.- J. Godjevac, N. Steele: Neuro-Fuzzy Control for Basic Mobile Robot Behaviours.- F. Hoffmann: The Role of Fuzzy Logic Control in Evolutionary Robotics.- Coordination of Behaviors: F.G. Pin, Y. Watanabe: Resolving Conflicts Between Behaviors Using Suppression and Inhibition.- S.G. Goodridge, M.G. Kay: Multi-Layered Fuzzy Behavior Fusion for Reactive Control of Autonomous Robots.- E.W. Tunstel Jr.: Fuzzy-Behavior Synthesis, Coordination, and Evolution in an Adaptive Behavior Hierarchy.- P. Pirjanian, M. Mataric: Multiple Objective vs. Fuzzy Behavior Coordination.- Mapping the Environment: E. Fabrizi, g. Oriolo, G. Ulivi: Accurate Map Building via Fusion of Laser and Ultrasonic Range Measures.- M. Lopez-Sanchez, R. Lopez de Mantaras, C. Sierra: Map Genaration by Co-operative Autonomous Robots Using Possibility Theory.- J. Gasos: Integrating Linguistic Descriptions and Sensor Observations for the Navigation of of Autonomous Robots.- Layer Integration: H. Surmann, L. Peters: MORIA: A Robot with Fuzzy Controlled Behaviour.- J. Zhang, A. Knoll: Integrating Deliberative and Reactive Strategies via Fuzzy Modular Control.