Robots are increasingly operating in indoor environments designed for and shared with people. However, robots working safely and autonomously in uneven and unstructured environments still face great challenges. Many modern indoor environments are designed with wheelchair accessibility in mind. This presents an opportunity for wheeled robots to navigate through sloped areas while avoiding staircases. In this paper, we present an integrated software and hardware system for autonomous mobile robot navigation in uneven and unstructured indoor environments. This modular and reusable software framework incorporates capabilities of perception and navigation. Our robot first builds a 3D OctoMap representation for the uneven environment with the 3D mapping using wheel odometry, 2D laser and RGB-D data. Then we project multilayer 2D occupancy maps from OctoMap to generate the the traversable map based on layer differences. The safe traversable map serves as the input for efficient autonomous navigation. Furthermore, we employ a variable step size Rapidly Exploring Random Trees that could adjust the step size automatically, eliminating tuning step sizes according to environments. We conduct extensive experiments in simulation and real-world, demonstrating the efficacy and efficiency of our system.
@cite_26: AbmmI-This paper explains a mew tour-guide robot Jinny. The Jinny is developed by focusing on human robot interaction and autonomous navigatiou. In order to achieve reliable and safe navigation performance, an integrated navigation strategy is established based on the analysis of a robot’s states and the decision making process of robot behaviors. Accordmg to the condition of environments, the robot can select its motion algorithm among four types of navigation strategy. Also, we emphasized the manageability of a robot’s knowledge base for human friendly interactions. The robot’s knowledge base can be extended or modilied intuitively enough to he managed by nonexperts. In order to show the feasibility and effectiveness of our system, we also present experimental results of the navigation system and some experiences on practical installations.
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@cite_27: State-of-the-art methods have recently achieved impressive performance for recognising the objects present in large databases of pre-collected images. There has been much less focus on building embodied systems that recognise objects present in the real world. This paper describes an intelligent system that attempts to perform robust object recognition in a realistic scenario, where a mobile robot moving through an environment must use the images collected from its camera directly to recognise objects. To perform successful recognition in this scenario, we have chosen a combination of techniques including a peripheral-foveal vision system, an attention system combining bottom-up visual saliency with structure from stereo, and a localisation and mapping technique. The result is a highly capable object recognition system that can be easily trained to locate the objects of interest in an environment, and subsequently build a spatial-semantic map of the region. This capability has been demonstrated during the Semantic Robot Vision Challenge, and is further illustrated with a demonstration of semantic mapping. We also empirically verify that the attention system outperforms an undirected approach even with a significantly lower number of foveations.
@cite_31: This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva’s software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. This article describes Minerva’s major software components, and provides a comparative analysis of the results obtained in the Smithsonian museum. During two weeks of highly successful operation, the robot interacted with thousands of people, both in the museum and through the Web, traversing more than 44km at speeds of up to 163 cm sec in the unmodie d museum.
@cite_17: This paper describes the software architecture of an autonomous tour-guideltutor robot. This robot was recently deployed in the "Deutsches Museum Bonn," were it guided hundreds of visitors through the museum during a six-day deployment period. The robot's control software integrates low-level probabilistic reasoning with high-level problem solving embedded in first order logic. A collection of software innovations, described in this paper, enabled the robot to navigate at high speeds through dense crowds, while reliably avoiding collisions with obstacles--some of which could not even be perceived. Also described in this paper is a user interface tailored towards non-expert users, which was essential for the robot's success in the museum. Based on these experiences, this paper argues that time is ripe for the development of AI-based commercial service robots that assist people in everyday life.
RHINO @cite_17 integrates a distributed autonomous navigation architecture dedicated to human robot interaction with various sensors. Minerva @cite_31 focuses on the probabilistic paradigm of environment, robot , sensors and models, setting the foundation for the probabilistic robotics epoch. Besides autonomous navigation, Robox @cite_9 integrated multi-modal interaction, speech recognition, and multi-robot coordination. Curious George @cite_27 could use visual attention locate the objects of interest in an environment. Jinny @cite_26 can select the proper motion strategy according to different environments. Sonar or laser range finders are usually positioned on these robot to build 2D maps along a horizontal slice of the world @cite_21 . However, just one slice of the space can not represent the environment, especially uneven environments with slopes and staircases.