Adaptive Execution in Complex Dynamic Worlds Robert James Firby Yale University 1989 A robot acting in the real world must use flexible plans because actions will sometimes fail to produce desired effects, and unexpected events will sometimes demand the robot shift its attention. A plan is usually construed as a list of primitive robot actions to be executed one after another but in a complex domain, a plan must be structured to cope effectively with the myriad unpredictable details it will encounter during execution. However, adding structure to a plan involves more than augmenting the primitive plan representation; it requires a complete model of interaction with the world called situation-driven execution. Situation-driven execution assumes that a plan consists of tasks with three major components: a satisfaction test, a window of activity, and a set of execution methods that are appropriate in different circumstances. Execution of such a plan proceeds by selecting an unsatisfied t...

Geometric uncertainty is unavoidable when programming robots for physical applications. We propose a stochastic framework for manipulation planning where plans are ranked on the basis of expected cost. That is, we express the desirability of states and actions with a cost function and describe uncertainty with probability distributions. We illustrate the approach with a new design for a programmable parts feeder, a mechanism that orients two-dimensional parts using a sequence of open-loop mechanical motions. We present a planning algorithm that accepts an n-sided polygonal part as input and, in time O(n²), generates a stochastically optimal plan for orienting the part.

Traditional approaches to task planning assume that the planner has access to all of the world information needed to develop a complete, correct plan which can then be executed in its entirety by an agent. Since this assumption does not typically hold in realistic domains, we have implemented a planner which can plan to perform sensor operations to allow an agent to gather the information necessary to complete planning and achieve its goals in the face of missing or uncertain environmental information. Naturally this approach requires some execution to be interleaved with the planning process. In this paper we present the results of a systematic experimental study of this planner's performance under various conditions. The chief difficulty arises when the agent performs actions which interfere with or, in the worst case, preclude the possibility of the achievement of its later goals. We have found that by making intelligent decisions about goal ordering, what to sense, and when to sens...

This paper addresses the problem of planning the motions of a circular mobile robot moving amidst polygonal obstacles with uncertainty in robot control and sensing. The robot is equipped with sensors which, if properly used, may provide information to overcome the uncertainty accumulated during the motions. The position sensor is based on dead-reckoning, the error then results into a cumulative uncertainty. A proximity sensor may be used to localize the robot with respect to the obstacles of the environment. The robot can also gain information by entering inside landmark areas where the position error is assumed to be bounded. We describe a planner which produces robust motion strategies composed of sensor-based motion commands which guarantee that, given an explicit model of the error accumulated by the motion commands, the robot can reach safely its goal with an error lower than a pre-specified value. It is based on a propagation of a numerical potential and on a geometric analysis o...

Abstract not found

In this paper we develop a formal computational theory of high-level linguistic communication that serves as a foundation for understanding cooperative action in groups of autonomous agents. We do so by examining and describing how messages affect the planning process and thereby relating communication to the intentions of the agents. We start by developing an abstract formal theory of knowledge representation based on the concept of information. We distinguish two types of information: state information, which describes the agent's knowledge about its world (knowing that) and process information, which describes the agent's knowledge of how to achieve some goal (knowing how). These two types of information are then used to formally define the agent's representation of knowledge states including the agent's intentional states. We then Show how situations and actions are related to the knowledge states. Using these relations we define a formal situation semantics for a propositional language. Based on this semantics, a formal pragmatic interpretation of the language is defined that formally describes how any given knowledge representational state is modified by a given message. Finally, using this theory of meaning of messages or speech acts, a theory of cooperation by means of communication is described.

Traditional approaches to task planning assume the planner has access to all of the world information needed to develop a complete, correct plan--a plan which can then be executed in its entirety by a robot. We consider prob- lems where some crucial information is missing at plan time but can be obtained from sensors during execution. We discuss the solution of these problems through deferred planning (i.e., by deferring specific planning steps until more complete information is available and then restarting the planner). We also present early results of a comparative study of strategies for deciding which plan steps to defer.

This paper reports on a recent work we have conducted concerning the development and the implementation of a robust motion planner for a mobile robot in a polygonal environment and in presence of uncertainty in robot control and sensing. Such a planner takes explicitly into account the uncertainty in robot control and produces a robust motion plan composed of sensor-based motion commands. The main originality of this approach is that it is able to account for a set of motion commands which may accumulate errors. It is based on a geometric analysis of the reachability and it generates motion strategies which may allow the robot to reduce its uncertainty. 1 Introduction While the problem of planning collision-free motions has received considerable attention, there has been only a limited number of contributions where uncertainties are taken explicitly into account in the planning process. However, uncertainties arising from sensor noise, control error and inaccurate models, constitute ...

Geometry will play a central role in the development of intelligent robotic systems. We describe a geometry engine, called robmod, that has been designed specifically for intelligent robotics applications. robmod is usable both as a geometric modelling system, based on the constructive solid geometry paradigm, and as a geometry engine that can be interrogated by programs that are reasoning about space. robmod is thus able to reduce the geometric sophistication required in such spatial reasoning programs. This paper originally appeared in Int. Conf. Robotics & Automation, Philadelphia, April 1988, pages 880--885. c flIEEE 1988: reformatted for the WWW 1996. Contact address: Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford OX1 3QD, UK. Phone: +44 1865 273850. E-mail Stephen.Cameron@comlab.ox.ac.uk 1 Introduction Geometry will play a central role in the development of intelligent robotic systems [5]. For example, collision detection and avoidance are ...

As planning technology improves, Artificial Intelligence planners are being embedded in increasingly complicated environments: ones that are particularly challenging even for human experts. Consequently, failure is becoming both increasingly likely for these systems (due to the difficult and dynamic nature of the new environments) and increasingly important to address (due to the systems' potential use on real world applications). This paper describes the development of a failure recovery component for a planner in a complex simulated environment and a procedure (called Failure Recovery Analysis) for assisting programmers in debugging that planner. The failure recovery design is iteratively enhanced and evaluated in a series of experiments. Failure Recovery Analysis is described and demonstrated on an example from the Phoenix planner. The primary advantage of these approaches over existing approaches is that they are based on only a weak model of the planner and its environment, which ...