Guy Ramel

Robox at Expo.02: A large-scale installation of personal robots

In this paper we present Robox, a mobile robot designed for operation in a mass exhibition and the experience we made with its installation at the Swiss National Exhibition Expo.02. Robox is a fully autonomous mobile platform with unique multi-modal interaction capabilities, a novel approach to global localization using multiple Gaussian hypotheses, and a powerful obstacle avoidance. Eleven Robox ran for 12 hours daily from May 15 to October 20, 2002, traveling more than 3315 km and interacting with 686,000 visitors.

On developing a voice-enabled interface for interactive tour-guide robots

This paper considers design methodologies in order to develop voice-enabled interfaces for tour-guide robots deployed at the Robotics Exposition of the Swiss National Exhibition (Expo.02). Human-robot voice communication presents new challenges for design of fully autonomous mobile robots, in that interactivity must be robot-initiated in conversation and within a dynamic adverse environment. We approached these general problems for a voice-enabled interface, tailored to limited computational resources of one on-board processor, when integrating smart speech signal acquisition, automatic speech recognition and synthesis, as well as a dialogue system into the multi-modal, multi-sensor interface for the expo tour-guide robot. We also focus on particular issues that needed to be addressed in voice-based interaction when planning specific tasks and research experiments for Expo.02, where tour-guide robots had to interact with hundreds of thousands of visitors over 6 months, 7 days a week, 10 h per day.

Detecting Semi-static Objects with a Laser Scanner

A method to register dynamic and semi-static objects with an a-priori known static map is proposed. Candidates for semi-static objects are extracted from laser data based on a-priori information (shape and size) of common indoor objects. Expectation Maximization serves at the same time scan alignment and data association between scan data andthe static a-priori map and the semi-static map. Dynamic objects are identified as outliers in data association. Evidence of visible but unmatched parts is gathered using recursive Bayesian updates to yield reliable candidate rejection in the presence of sensor noise. This allows the resulting semi-static map to adapt to changes in the environment, which is demonstrated experimentally.

Probabilistic Contextual Situation Analysis

Mobile robots are gradually appearing in our daily environment. To navigate autonomously in real-world environments and interact with objects and humans, robots face various major technological challenges. Among the required key competencies of such robots is their ability to perceive the environment and reason about it, to plan appropriate actions. However, sensory information perceived from real-world situations is error prone and incomplete and thus often results in ambiguous interpretations. We propose a new approach for object recognition that incorporates visual and range information with spatial arrangement between objects (context information). It is based on using Bayesian networks to fuse and infer information from different data.In the proposed framework, we first extract potential objects from the scene image using simple features- characteristics like colour or the relation between height and width. This basic information is easy to extract but often results in ambiguous situations between similar objects. To resolve ambiguities among the detected objects, the relative spatial arrangement (context information) of the objects is used in a second step. Consider, for example, a cola can and a red trash can that are both cylindrical, have similar ratios between width and height and have very similar colours. Depending on their distances from the robot, they may be hard to distinguish. However, if we further consider their spatial arrangement with other objects, e.g. a table, they might be clearly differentiable, the cola can typically standing on the table and the trash can on the floor. This contextual information is therefore a very efficient way to increase drastically the reliability of object recognition and scene interpretation. Moreover, range information from a laser scanner and speech recognition offer complementary information to improve reliability further. Thus, an approach using laser range data to recognize places (such as corridors, crossings, rooms and doors) using Bayesian programming is also developed for both topological navigation in a typical indoor environment and object recognition.