Cutting Down the Energy Consumed by Domestic Robots: Insights from Robotic Vacuum Cleaners

The market of domestic service robots, and especially vacuum cleaners, has kept growing during the past decade. According to the International Federation of Robotics, more than 1 million units were sold worldwide in 2010. Currently, there is no in-depth analysis of the energetic impact of the introduction of this technology on the mass market. This topic is of prime importance in our energy-dependant society. This study aims at identifying key technologies leading to the reduction of the energy consumption of a domestic mobile robot, by exploring the design space using technologies issued from the robotic research field, such as the various localization and navigation strategies. This approach is validated through an in-depth analysis of seven vacuum cleaning robots. These results are used to build a global assessment of the influential parameters. The major outcome is the assessment of the positive impact of both the ceiling-based visual localization and the laser-based localization approaches

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Robot Perception of Static and Dynamic Objects with an Autonomous Floor Scrubber

This paper presents the perception system of a new professional cleaning robot for large public places. The proposed system is based on multiple sensors including 3D and 2D lidar, two RGB-D cameras and a stereo camera. The two lidars together with an RGB-D camera are used for dynamic object (human) detection and tracking, while the second RGB-D and stereo camera are used for detection of static objects (dirt and ground objects). A learning and reasoning module for spatial-temporal representation of the environment based on the perception pipeline is also introduced. Furthermore, a new dataset collected with the robot in several public places, including a supermarket, a warehouse and an airport, is released.Baseline results on this dataset for further research and comparison are provided. The proposed system has been fully implemented into the Robot Operating System (ROS) with high modularity, also publicly available to the community

Towards autonomous design of experiments for robots

For understanding a real-world environment on a conceptual level, any agent requires the capability for autonomous, open-ended learning. One of the main challenges in Artificial Intelligence is to bias the learning phase sufficiently in order to obviate complexity issues, while at the same time not restricting the agent to a certain environment or to a particular task. In this paper we describe a framework for autonomous design of experiments for a robotic agent, which enables the robot to improve and increase its conceptual knowledge about the environment through open-ended learning by experimentation. We specify our implementation of this framework and describe how its modules can recognize situations in which learning is useful or necessary, gather target-oriented data and provide it to machine learning algorithms, thus reducing the search space for the learning target significantly. We describe the integration of these modules and the real world scenarios in which we tested them

Requirements for a Reference Dataset for Multimodal Human Stress Detection

Stress is necessary for optimal performance and functioning in daily life. However, when stress exceeds person-specific coping levels, then it begins to negatively impact health and productivity. An automatic stress monitoring system that tracks stress levels based on physical and physiological parameters, can assist the user in maintaining stress within healthy limits. In order to build such a system, we need to develop and test various algorithms on a reference dataset consisting of multimodal stress responses. Such a reference dataset should fulfil requirements derived from results and practices of clinical and empirical research. This paper proposes a set of such requirements to support the establishment of a reference dataset for multimodal human stress detection. The requirements cover person-dependent and technical aspects such as selection of sample population, choice of stress stimuli, inclusion of multiple stress modalities, selection of annotation methods, and selection of data acquisition devices. Existing publicly available stress datasets were evaluated based on criteria derived from the proposed requirements. It was found that none of these datasets completely fulfilled the requirements. Therefore, efforts should be made in the future to establish a reference dataset, satisfying the specified requirements, in order to ensure comparability and reliability of results

Autonomous design of experiments for learning by experimentation

In Artificial Intelligence, numerous learning paradigms have been developed over the past decades. In most cases of embodied and situated agents, the learning goal for the artificial agent is to \u201emap\u201c or classify the environment and the objects therein [1, 2], in order to improve navigation or the execution of some other domain-specific task. Dynamic environments and changing tasks still pose a major challenge for robotic learning in real-world domains. In order to intelligently adapt its task strategies, the agent needs cognitive abilities to more deeply understand its environment and the effects of its actions. In order to approach this challenge within an open-ended learning loop, the XPERO project (http://www.xpero.org) explores the paradigm of Learning by Experimentation to increase the robot's conceptual world knowledge autonomously. In this setting, tasks which are selected by an actionselection mechanism are interrupted by a learning loop in those cases where the robot identifies learning as necessary for solving a task or for explaining observations. It is important to note that our approach targets unsupervised learning, since there is no oracle available to the agent, nor does it have access to a reward function providing direct feedback on the quality of its learned model, as e.g. in reinforcement learning approaches. In the following sections we present our framework for integrating autonomous robotic experimentation into such a learning loop. In section 1 we explain the different modules for stimulation and design of experiments and their interaction. In section 2 we describe our implementation of these modules and how we applied them to a real world scenario to gather target-oriented data for learning conceptual knowledge. There we also indicate how the goaloriented data generation enables machine learning algorithms to revise the failed prediction model

Mobile Robots In Office Logistics

In this paper, we describe an office logistic robot designed to execute tasks such as collecting and delivering mail, delivering print outs, and collecting waste baskets. Our concern is not so much the development of new methods,since for most of the problems which we had to address we found theoretical solutions in the literature. Rather we are concerned with adapting these solutions to real-world surroundings with little or no modifications of the environment, and combining them into a system which provides a variety of services. In the following sections we describe the system's various components, as well as its overall architecture. We conclude with a discussion of an experiment which demonstrates the performance of our system. INTRODUCTION The technical and scientific progress made in robotics over the last two decades suggests that the field is ready to create robots for applications outside of industrial production processes. The use of robot technology is particularly desira..

SSRR 2004, IEEE International Workshop on Safety, Security, and Rescue Robotics. Proceedings. CD-ROM: May 24-26, 2004, Gustav Stresemann-Institut Bonn, Germany

The SSRR workshop has been dedicated to identifying and elaborating on the key scientific issues, not limited to: Mobility, Sensors, Perception, Human-robot Interaction, Distributed Intelligence and Communication The workshop will be structured by four main application-oriented threads: Resource Coordination and Information Management in Disaster Scenarios; Security and Safety Issues (airports, screening, surveillance robots, etc.); Fire Fighting and Collapsed Buildings (search and rescue, chemicals, etc.); Flooding Rivers (support of task forces, resource planning, geo-data, etc.) Researchers in the field of safety, security and rescue robotics cannot work in a vacuum. As with SSRR04, a large part of this workshop will be dedicated to talks from rescue workers, police, and government agencies who have extensive field experience to gain an understanding of what robotics can do for them