Alberto Elfes

Using occupancy grids for mobile robot perception and navigation

An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid is reviewed. The occupancy grid is a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice. To construct a sensor-derived map of the robots world, the cell state estimates are obtained by interpreting the incoming range readings using probabilistic sensor models. Bayesian estimation procedures allow the incremental updating of the occupancy grid, using readings taken from several sensors over multiple points of view. The use of occupancy grids from mapping and for navigation is examined. Operations on occupancy grids and extensions of the occupancy grid framework are briefly considered.<<ETX>>

High resolution maps from wide angle sonar

We describe the use of multiple wide-angle sonar range measurements to map the surroundings of an autonomous mobile robot. A sonar range reading provides information concerning empty and occupied volumes in a cone (subtending 30 degrees in our case) in front of the sensor. The reading is modelled as probability profiles projected onto a rasterized map, where somewhere occupied and everywhere empty areas are represented. Range measurements from multiple points of view (taken from multiple sensors on the robot, and from the same sensors after robot moves) are systematically integrated in the map. Overlapping empty volumes re-inforce each other, and serve to condense the range of occupied volumes. The map definition improves as more readings are added. The final map shows regions probably occupied, probably unoccupied, and unknown areas. The method deals effectively with clutter, and can be used for motion planning and for extended landmark recognition. This system has been tested on the Neptune mobile robot at CMU.

Sonar-based real-world mapping and navigation

A sonar-based mapping and navigation system developed for an autonomous mobile robot operating in unknown and unstructured environments is described. The system uses sonar range data to build a multileveled description of the robots surroundings. Sonar readings are interpreted using probability profiles to determine empty and occupied areas. Range measurements from multiple points of view are integrated into a sensor-level sonar map, using a robust method that combines the sensor information in such a way as to cope with uncertainties and errors in the data. The resulting two-dimensional maps are used for path planning and navigation. From these sonar maps, multiple representations are developed for various kinds of problem-solving activities. Several dimensions of representation are defined: the abstraction axis, the geographical axis, and the resolution axis. The sonar mapping procedures have been implemented as part of an autonomous mobile robot navigation system called Dolphin. The major modules of this system are described and related to the various mapping representations used. Results from actual runs are presented, and further research is mentioned. The system is also situated within the wider context of developing an advanced software architecture for autonomous mobile robots.

Integration of sonar and stereo range data using a grid-based representation

The authors use occupancy grids to combine range information from sonar and one-dimensional stereo into a two-dimensional map of the vicinity of a robot. Each cell in the map contains a probabilistic estimate of whether it is empty or occupied by an object in the environment. These estimates are obtained from sensor models that describe the uncertainty in the range data. A Bayesian estimation scheme is applied to update the current map using successive range readings from each sensor. The occupancy grid representation is simple to manipulate, treats different sensors uniformly, and models uncertainty in the sensor data and in the robot position. It also provides a basis for motion planning and creation of more abstract object descriptions.<<ETX>>

A sonar-based mapping and navigation system

This paper describes a sonar-based mapping and navigation system for autonomous mobile robots operating in unknown and unstructured surroundings. The system uses sonar range data to build a multileveled description of the robots environment. Sonar maps are represented in the system along several dimensions: the Abstraction axis, the Geographical axis, and the Resolution axis. Various kinds of problem-solving activities can be performed and different levels of performance can be achieved by operating with these multiple representations of maps. The major modules of the Dolphin system are described and related to the various mapping representations used. Results from actual runs are presented and further research is mentioned. The system is also situated within the wider context of developing an advanced software architecture for autonomous mobile robots.

A semi-autonomous robotic airship for environmental monitoring missions

This paper discusses Project AURORA (autonomous unmanned remote monitoring robotic airship) which focuses on the development of the control, navigation, sensing, and inference technologies required for substantially autonomous robotic airships. Our target application areas include the use of robotic airships for environmental, biodiversity, and climate research and monitoring. Based on typical mission requirements, we present arguments that favour airships over airplanes and helicopters as the ideal platforms for such missions. We outline the overall system architecture of the AURORA robotic airship, discuss its main subsystems, and mention the research and development issues involved.

Sensor integration for robot navigation: Combining sonar and stereo range data in a grid-based representataion

Multiple range sensors are essential in mobile robot navigation systems. This introduces the problem of integrating noisy range data from multiple sensors and multiple robot positions into a common description of the environment. We propose a cellular representation called the occupancy grid as a solution to this problem. In this paper, we use occupancy grids to combine range information from sonar and one-dimensional stereo into a two-dimensional map of the vicinity of a robot. Each cell in the map contains a probabilistic estimate of whether it is empty or occupied by an object in the environment. These estimates are obtained from sensor models that describe the uncertainty in the range data. A Bayesian estimation scheme is used to update the existing map with successive range profiles from each sensor. This representation is simple to manipulate, treats different sensors uniformly, and models uncertainty in the sensor data and in the robot position. It also provides a basis for motion planning and creation of higherlevel object descriptions.

Robotic Airships for Exploration of Planetary Bodies with an Atmosphere: Autonomy Challenges

Robotic unmanned aerial vehicles have great potential as surveying and instrument deployment platforms in the exploration of planets and moons with an atmosphere. Among the various types of planetary aerovehicles proposed, lighter-than-atmosphere (LTA) systems are of particular interest because of their extended mission duration and long traverse capabilities. In this paper, we argue that the unique characteristics of robotic airships make them ideal candidates for exploration of planetary bodies with an atmosphere. Robotic airships extend the capabilities of balloons through their flight controllability, allowing (1) precise flight path execution for surveying purposes, (2) long-range as well as close-up ground observations, (3) station-keeping for long-term monitoring of high science value sites, (4) transportation and deployment of scientific instruments and in situ laboratory facilities across vast distances, and (5) opportunistic flight path replanning in response to the detection of relevant sensor signatures. Implementation of these capabilities requires achieving a high degree of vehicle autonomy across a broad spectrum of operational scenarios. The paper outlines some of the core autonomy technologies required to implement the capabilities listed above, drawing on work and results obtained in the context of AURORA (Autonomous Unmanned Remote Monitoring Robotic Airship), a research effort that focuses on the development of the technologies required for substantially autonomous robotic airships. We discuss airship modeling and control, autonomous navigation, and sensor-based flight control. We also outline an approach to airborne perception and monitoring which includes mission-specific target acquisition, discrimination and identification, and present experimental results obtained with AURORA.

Tracking Multiple Moving Objects for Real-Time Robot Navigation

This paper proposes a method for detecting and tracking the motion of a large number of dynamic objects in crowded environments, such as concourses in railway stations or airports, shopping malls, or convention centers. With this motion information, a mobile vehicle is able to navigate autonomously among moving obstacles, operating at higher speeds and using more informed locomotion strategies that perform better than simple reactive manoeuvering strategies. Unlike many of the methods for motion detection and tracking discussed in the literature, our approach is not based on visual imagery but uses 2D range data obtained using a laser rangefinder. The direct availability of range information contributes to the real-time performance of our approach, which is a primary goal of the project, since the purpose of the vehicle is the transport of humans in crowded areas. Motion detection and tracking of dynamic objects is done by constructing a sequence of temporal lattice maps. These capture the time-varying nature of the environment, and are denoted as time-stamp maps. A time-stamp map is a projection of range information obtained over a short interval of time (a scan) onto a two-dimensional grid, where each cell which coincides with a specific range value is assigned a time stamp. Based on this representation, we devised two algorithms for motion detection and motion tracking. The approach is very efficient, with a complete cycle involving both motion detection and tracking taking 6 ms on a Pentium 166 MHz. The system has been demonstrated on an intelligent wheelchair operating in railway stations and convention centers during rush hour.

A distributed control architecture for an autonomous mobile robot

Abstract This paper describes a Distributed Control Architecture for an autonomous mobile robot. We start by characterizing the Conceptual Levels into which the various problem-solving activities of a mobile robot can be classified. In sequence, we discuss a Distributed Control System that provides scheduling and coordination of multiple concurrent activities on a mobile robot. Multiple Expert Modules are responsible for the various tasks and communicate through messages and over a Blackboard. As a testbed, the architecture of a specific system for Sonar-Based Mapping and Navigation is presented, and a distributed implementation is described.