Mobile robot navigation

About: Mobile robot navigation is a research topic. Over the lifetime, 14713 publications have been published within this topic receiving 263092 citations.

Hands-free multi-scale navigation in virtual environments

Abstract: This paper presents a set of interaction techniques for hands-free multi-scale navigation through virtual environments. We believe that hands-free navigation, unlike the majority of navigation techniques based on hand motions, has the greatest potential for maximizing the interactivity of virtual environments since navigation modes are offloaded from modal hand gestures to more direct motions of the feet and torso. Not only are the users’ hands freed to perform tasks such as modeling, notetaking and object manipulation, but we also believe that foot and torso movements may inherently be more natural for some navigation tasks. The particular interactions that we developed include a leaning technique for moving small and medium distances, a foot-gesture controlled Step WIM that acts as a floor map for moving larger distances, and a viewing technique that enables a user to view a full 360 degrees in only a three-walled semi-immersive environment by subtly amplifying the mapping between their torso rotation and the virtual world. We formatively designed and evaluated our techniques in existing projects related to archaeological reconstructions, free-form modeling, and interior design. In each case, our informal observations have indicated that motions such as walking and leaning are both appropriate for navigation and are effective in cognitively simplifying complex virtual environment interactions since functionality is more evenly distributed across the body.

Fast vision-guided mobile robot navigation using model-based reasoning and prediction of uncertainties

Abstract: The model-based vision system described in this paper allows a mobile robot to navigate indoors at an average speed of 8 to 10 m/min using ordinary laboratory computing hardware of approximately 16 MIPS power. The navigation capabilities of the robot are not impaired by the presence of stationary or moving obstacles. The vision system maintains a model of uncertainty and keeps track of the growth of uncertainty as the robot travels toward the goal position. The estimates of uncertainty are then used to predict bounds on the locations and orientations of landmarks expected to be seen in a monocular image. This greatly reduces the search for establishing correspondence between the features visible in the image and the landmarks. Given a sequence of image features and a sequence of landmarks derived from a geometric model of the environment, a special aspect of our vision system is the sequential reduction in the uncertainty as each image feature is matched successfully with a landmark, allowing subsequent features to be matched more easily; this is a natural by-product of the manner in which we use Kalman filter-based updating.

Mobile work robot system

Abstract: The system includes a mobile work robot and a separate station. The mobile robot is equipped to perform prescribed tasks, such as cleaning building floors. The station is equipped to remotely control the movement of the mobile work robot and to perform maintenance on the mobile work robot, such as the replacement of parts as well as replenishment of consumable goods necessary for the mobile work robot to move and work. In addition, the cleaning means equipped on the station performs the cleaning and disinfection of the mobile work robot.

An experimental comparison of localization methods continued

Abstract: Localization is one of the fundamental problems in mobile robot navigation. Past experiments have shown that, in general, grid-based Markov localization is more robust than Kalman filtering while the latter can be more accurate than the former Recently new methods for localization employing particle filters have become popular. In this paper, we compare different localization methods using Kalman filtering, grid-based Markov localization, Monte Carlo Localization (MCL), and combinations thereof. We give experimental evidence that a combination of Markov localization and Kalman filtering as well as a variant of MCL outperform the other methods in terms of accuracy, robustness, and time needed for recovering from manual robot displacement, while requiring only few computational resources.

Learning and interacting in human-robot domains

Abstract: We focus on a robotic domain in which a human acts both as a teacher and a collaborator to a mobile robot. First, we present an approach that allows a robot to learn task representations from its own experiences of interacting with a human. While most approaches to learning from demonstration have focused on acquiring policies (i.e., collections of reactive rules), we demonstrate a mechanism that constructs high-level task representations based on the robot's underlying capabilities. Next, we describe a generalization of the framework to allow a robot to interact with humans in order to handle unexpected situations that can occur in its task execution. Without using explicit communication, the robot is able to engage a human to aid it during certain parts of task execution. We demonstrate our concepts with a mobile robot learning various tasks from a human and, when needed, interacting with a human to get help performing them.