Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. This paper reports on first results of a research program that uses partially observable Markov models to robustly track a robot's location in office environments and to direct its goal-oriented actions. The approach explicitly maintains a probability distribution over the possible locations of the robot, taking into account various sources of uncertainty, including approximate knowledge of the environment, and actuator and sensor uncertainty. A novel feature of our approach is its integration of topological map information with approximate metric information. We demonstrate the robustness of this approach in controlling an actual indoor mobile robot navigating corridors. 1 Introduction We are interested in the task of long-term autonomous navigation in an office environment (with corridors, foyers, and rooms). While the state of the art in autonomous office nav...

Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. We present a technique for achieving this goal that uses partially observable Markov decision process models (POMDPs) to explicitly model navigation uncertainty, including actuator and sensor uncertainty and approximate knowledge of the environment. This allows the robot to maintain a probability distribution over its current pose. Thus, while the robot rarely knows exactly where it is, it always has some belief as to what its true pose is, and is never completely lost. We present a navigation architecture based on POMDPs that provides a uniform framework with an established theoretical foundation for pose estimation, path planning, robot control during navigation, and learning. Our experiments show that this architecture indeed leads to robust corridor navigation for an actual indoor mobile robot. 1

We present a new method for local obstacle avoidance by indoor mobile robots that formulates the problem as one of constrained optimization in velocity space. Constraints that stem from physical limitations (velocities and accelerations) and the environment (the configuration of obstacles) are placed on the translational and rotational velocities of the robot. The robot chooses velocity commands that satisfy all the constraints and maximize an objective function that trades off speed, safety and goaldirectedness. An efficient, real-time implementation of the method has been extensively tested, demonstrating reliable, smooth and speedy navigation in office environments. The obstacle avoidance method is used as the basis of more sophisticated navigation behaviors, ranging from simple wandering to map-based navigation. 1 Introduction We address the problem of local obstacle avoidance for mobile robots operating in unknown, or partially known, environments. While this problem has been stu...

Autonomous mobile robots need very reliable navigation capabilities in order to operate unattended for long periods of time. We have developed an approach that uses partially observable Markov models to robustly track a robot’s location and integrates it with a planning and execution monitoring approach that uses this information to control the robot’s actions. The approach explicitly maintains a probability distributionover the possiblelocations of the robot, taking into account various sources of uncertainty, including approximate knowledge of the environment, actuator uncertainty, and sensor noise. A novel feature of our approach is its integration of topological map information with approximate metric information. We demonstrate the reliability of this approach, especially its ability to smoothly recover from errors in sensing. 1.

Office delivery robots have to perform many tasks. They have to determine the order in which to visit ofces, plan paths to those offices, follow paths reliably, and avoid static and dynamic obstacles in the process. Reliability and efficiency are key issues in the design of such autonomous robot systems. They must deal reliably with noisy sensors and actuators and with incomplete knowledge of the environment. They must also act efficiently, in real time, to deal with dynamic situations. Our architecture is composed of four abstraction layers: obstacle avoidance, navigation, path planning, and task scheduling. The layers are independent, communicating processes that are always active, processing sensory data and status information to update their decisions and actions. A version of our robot architecture has been in nearly daily use in our building since December 1995. As of July 1996, the robot has traveled more than 75 kilometers in service of over 1800 navigation requests that were specified using our World Wide Web interface.

This paper describes Rogue, anintegrated planning and executing robotic agent. Rogue is designed to be aroving o ce gopher unit, doing tasks such as picking up & delivering mail and returning & picking up library books, in a setup where users can post tasks for the robot to do. We have been working towards the goal of building a completely autonomous agent which can learn from its experiences and improve uponits own behaviour with time. This paper describes what we have achieved to-date: (1) a system that can generate and execute plans for multiple interacting goals which arrive asynchronously and whose task structure is not known a priori, interrupting and suspending tasks when necessary, and (2) a system which can compensate for minor problems in its domain knowledge, monitoring execution to determine when actions did not achieve expected results, and replanning to correct failures. 1.

khaigh�cs.cmu.edu

1 Introduction Modularity Issues in Reactive Planning The RAP reactive plan execution system is specifically designed to accept plans and goals at a high level of abstraction and expand them into detailed actions at run time. One of the key features of the RAP system is the use of hierarchical expansion methods that allow different paths of execution for the same goals in different situations. Another central reason for the hierarchy is to create modular expansion methods that can be used in the execution of many different tasks. However, experience using the RAP system to control the University of Chicago robot Chip in the 1995 IJCAI robot competition has shown that there are difficult trade-offs between modularity and correctness in a predefined plan hierarchy. This paper describes the RAP hierarchies used to control the robot while cleaning up a small office space and discusses some of the issues raised in writing these RAPs to be useful for other tasks as well...

This paper presents a general approach to robust execution monitoring. The goal is to provide coverage for many types of unexpected and unanticipated situations, while at the same time enabling the robot to quickly detect, and react to, specific contingencies. The approach uses a hierarchy of monitors, structured in layers of increasing specificity. We present the general approach, and show its application in the domain of indoor mobile robot navigation. Introduction Mobile robots operating in the real world need very reliable navigation capabilities to operate autonomously for long periods of time. Because it is almost impossible for the programmer to predict all the circumstances that might be encountered, a mechanism to handle the unexpected is required. We have developed an exception detection and recovery architecture for this purpose. The architecture uses hierarchically structured monitors. The upper layers of monitors are more general: They detect large classes of exceptions,...

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