This article describes the software architecture of an autonomous, interactive tour-guide robot. It presents a modular and distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Web-based telepresence. At its heart, the software approach relies on probabilistic computation, on-line learning, and any-time algorithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot's operation. Special emphasis is placed on the design of interactive capabilities that appeal to people's intuition. The interface provides new means for human-robot interaction with crowds of people in public places, and it also provides people all around the world with the ability to establish a "virtual telepresence" using the Web. To illustrate our approach, results are reported obtained in mid-...

Renewed motives for space exploration have inspired NASA to work toward the goal of establishing a virtual presence in space, through heterogeneous effets of robotic explorers. Information technology, and Artificial Intelligence in particular, will play a central role in this endeavor by endowing these explorers with a form of computational intelligence that we call remote agents. In this paper we describe the Remote Agent, a specific autonomous agent architecture based on the principles of model-based programming, on-board deduction and search, and goal-directed closed-loop commanding, that takes a significant step toward enabling this future. This architecture addresses the unique characteristics of the spacecraft domain that require highly reliable autonomous operations over long periods of time with tight deadlines, resource constraints, and concurrent activity among tightly coupled subsystems. The Remote Agent integrates constraint-based temporal planning and scheduling, robust multi-threaded execution, and model-based mode identification and reconfiguration. The demonstration of the integrated system as an on-board controller for Deep Space One, NASA's rst New Millennium mission, is scheduled for a period of a week in late 1998. The development of the Remote Agent also provided the opportunity to reassess some of AI's conventional wisdom about the challenges of implementing embedded systems, tractable reasoning, and knowledge representation. We discuss these issues, and our often contrary experiences, throughout the paper.

firestorm of interest in autonomous robots with the introduction of the Subsumption architecture 1 [Brooks86]. At the time, the dominant view in the AI community was that a control system for an autonomous mobile robot should be decomposed into three functional elements: a sensing system, a planning system, and an execution system [Nilsson80]. The job of the sensing system is to translate raw sensor input (usually sonar or vision data) into a world model. The job of the planner is to take the world model and a goal and generate a plan to achieve the goal. The job of the execution system is to take the plan and generate the actions it prescribes. The sense-plan-act (SPA) approach has two significant architectural features. First, the flow of

Robot systems must achieve high level goals while remaining reactive to contingencies and new opportunities. This typically requires robot systems to coordinate concurrent activities, monitor the environment, and deal with exceptions. We have developed a new language to support such task-level control. The language, TDL, is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling. A compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library. This paper introduces TDL, describes the task tree representation that underlies the language, and presents some aspects of its implementation and use in an autonomous mobile robot. Introduction Robot systems, such as autonomous mobile robots, need to achieve high level goals while remaining reactive to contingencies and new opportunities. They need to recover gracefully from exceptions and effectively manage their resourc...

This paper describes an implemented robot system, which relies heavily on probabilistic AI techniques for acting under uncertainty. The robot Pearl and its predecessor Flo have been developed by a multi-disciplinary team of researchers over the past three years. The goal of this research is to investigate the feasibility of assisting elderly people with cognitive and physical activity limitations through interactive robotic devices, thereby improving their quality of life. The robot’s task involves escorting people in an assisted living facility—a timeconsuming task currently carried out by nurses. Its software architecture employs probabilistic techniques at virtually all levels of perception and decision making. During the course of experiments conducted in an assisted living facility, the robot successfully demonstrated that it could autonomously provide guidance for elderly residents. While previous experiments with fielded robot systems have provided evidence that probabilistic techniques work well in the context of navigation, we found the same to be true of human robot interaction with elderly people.

This paper describes the New Millennium Remote Agent #NMRA# architecture for autonomous spacecraft control systems. This architecture integrates traditional real-time monitoring and control with constraintbased planning and scheduling, robust multi-threaded execution, and model-based diagnosis and recon#guration.

This paper develops the Reactive Model-Based Programming Language (RMPL) and its executive, called Titan. RMPL provides the features of synchronous, reactive languages, with the added ability of reading and writing to state variables that are hidden within the physical plant being controlled. Titan executes an RMPL program using extensive component-based declarative models of the plant to track states, analyze anomalous situations, and generate novel control sequences. Within its reactive control loop, Titan employs propositional inference to deduce the system's current and desired states, and it employs model-based reactive planning to move the plant from the current to the desired state

In the future, webs of unmanned air and space vehicles will act together to robustly perform elaborate missions in uncertain environments. We coordinate these systems by introducing a reactive model-based programming language (RMPL) that combines within a single unified representation the flexibility of embedded programming and reactive execution languages, and the deliberative reasoning power of temporal planners. The KIRK planning system takes as input a problem expressed as a RMPL program, and compiles it into a temporal plan network (TPN), similar to those used by temporal planners, but extended for symbolic constraints and decisions. This intermediate representation clarifies the relation between temporal planning and causal-link planning, and permits a single task model to be used for planning and execution. Such a

This paper describes the Remote Agent flight experiment for spacecraft commanding and control. In the Remote Agent approach, the operational rules and constraints are encoded in the flight software. The software may be considered to be an autonomous "remote agent" of the spacecraft operators in the sense that the operators rely on the agent to achieve particular goals. The experiment will

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply `pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively.