Despite many decades of research into mobile robot control, reliable, high-speed motion in complicated, uncertain environments remains an unachieved goal. In this paper we present a solution to realtime motion control that can competently maneuver a robot at optimal speed even as it explores a new region or encounters new obstacles. The method uses a navigation function to generate a gradient field that represents the optimal (lowest-cost) path to the goal at every point in the workspace. Additionally, we present an integrated sensor fusion system that allows incremental construction of an unknown or uncertain environment. Under modest assumptions, the robot is guaranteed to get

Anchoring is the process of creating and maintaining the correspondence between symbols and percepts that refer to the same physical objects. Although this process must necessarily be present in any physically embedded system that includes a symbolic component (e.g., an autonomous robot), no systematic study of anchoring as a problem per se has been reported in the literature on intelligent systems. In this paper, we propose a domain-independent definition of the anchoring problem, and identify its three basic functionalities: find, reacquire, and track. We illustrate our definition on two systems operating in two different domains: an unmanned airborne vehicle for traffic surveillance; and a mobile robot for office navigation.

Effective task-level control is critical for robots that are to engage in purposeful activity in realworld environments. This paper describes PRSLite, a task-level controller grounded in a procedural knowledge approach to action description. The controller embodies much of the philosophy that underlies the Procedural Reasoning System (PRS) but in a minimalist fashion. Several features of PRS-Lite distinguish it from its predecessor, including a richer goal semantics and a generalized control regime. Both of these features are critical for supporting the management of continuous processes employed in current-generation robots. PRS-Lite has been used extensively as a task-level controller for a robot whose underlying behaviors are implemented as fuzzy rules. Tasks to which it has been applied include vision-based tracking, autonomous exploration, and complex delivery scenarios. Introduction In recent years, there has been a convergence of design methodologies for certain aspects of rob...

In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. Key words: cognitive architectures, intelligent systems, cognitive processes 1

We present an attempt to reconcile the theoretical work on reasoning about action with the realization of agents, in particular mobile robots. Specifically, we present a logical framework for representing dynamic systems based on description logics, which allows for the formalization of sensing actions. We address the generation of conditional plans by defining a suitable reasoning method in which a plan is extracted from a constructive proof of a query expressing a given goal. We also present an implementation of such a logical framework, which has been tested on the mobile robot "Tino".

this paper we are concerned with how a user can write sequencer programs to effectively control the robot. Our emphasis is on issues of language and semantics: what is a good language for robot programs, what kind of semantics is appropriate for the sequencer, and how does the language fit the semantics. The result of our inquiries is the sequencer language Colbert, a part of the Sapphira architecture.

Robotic Development Environments (RDEs) have come to play an increasingly important role in robotics research in general, and for the development of architectures for mobile robots in particular. Yet, no systematic evaluation of available RDEs has been performed; establishing a comprehensive list of evaluation criteria targeted at robotics applications is desirable that can subsequently be used to compare their strengths and weaknesses. Moreover, there are no practical evaluations of the usability and impact of a large selection of RDEs that provides researchers with the information necessary to select an RDE most suited to their needs, nor identifies trends in RDE research that suggest directions for future RDE development. This survey addresses the above by selecting and describing nine open source, freely available RDEs for mobile robots, evaluating and comparing them from various points of view. First, based on previous work concerning agent systems, a conceptual framework of four broad categories is established, encompassing the characteristics and capabilities that an RDE supports. Then, a practical evaluation of RDE usability in designing, implementing, and executing robot architectures is presented. Finally, the impact of specific RDEs on the field of robotics is addressed by providing a list of published applications and research projects that give concrete examples of areas in which systems have been used. The comprehensive evaluation and comparison of the nine RDEs concludes with suggestions of how to use the results of this survey and a brief discussion of future trends in RDE design. 1

In this article, we will present an overview of the Coupled Layered Architecture for Robotic Autonomy. CLARAty develops a framework for generic and reusable robotic components that can be adapted to a number of heterogeneous robot platforms. It also provides a hmework that will simplify the integration of new technologies and enable the comparison of various elements. CLARAty consists of two distinct layers: a FUIIC~~OM ~ Layer and a Decision Layer. The Functional Layer defines the various abstractions of the system and adapts the abstract components to real or simulated devices. It provides a-work and the algorithms for low- and mid-level autonomy. The Decision Layer provides the system’s high-level autonomy, which reasons about global resources and mission constraints. The Decision Layer accesses information from the Functional Layer at multiple levels of granularity. In this article, we will also present some of the challenges in developing interoperable software for various rover platforms. Examples will include challenges from the locomotion and manipulation domains

The focus of current research in cognitive robotics is both on the realization of systems based on known formal settings and on the extension of previous formal approaches to account for features that play a significant role for autonomous robots, but have not yet received an adequate treatment. In this paper we adopt a formal framework derived from Propositional Dynamic Logics by exploiting their formal correspondence with Description Logics, and present an extension of such a framework obtained by introducing both concurrency on primitive actions and autoepistemic operators for explicitly representing the robot’s epistemic state. We show that the resulting formal setting allows for the representation of actions with contextdependent effects, sensing actions, and concurrent actions, and address both the presence of exogenous events and the characterization of the notion of executable plan in such a complex setting. Moreover, we present an implementation of this framework in a system which is capable of generating plans that are actually executed on mobile robots, and illustrate the experimentation of such a system in the design and implementation of soccer players for the 1999 Robocup competition.

Abstract- We will present an overview of the CLARAty architecture which aims at developing reusable software components for robotic systems. These com-ponents are to support autonomy software which plans and schedules robot activities. The CLARAty architec-ture modifies the conventional three-level robotic archi-tecture into a new two-layered design: the Functional Layer and the Decision Layer. The Functional Layer pro-vides a representation of the system components and an implementation of their basic functionalities. The Deci-sion Layer is the decision making engine that drives the Functional Layer. It globally reasons about the intended goals, system resources, and state of the system and its environment. The Functional Layer is composed of a set of interrelated object-oriented hierarchies consisting of active and passive objects that represent the different levels of system abstractions. In this paper, we present an overview of the design of the Functional Layer. The Functional Layer is decomposed into a set of reusable core components and a set of extended components that adapt the reusable set to different hardware implemen-tations. The reusable components: (a) provide interface definitions and implementations of basic functionality, (b) provide local executive capabilities, (c) manage local resources, and (d) support state and resource queries by the Decision Layer. I.