Abstract-The vision of an Ecology of Physically EmbeddedIntelligent Systems, or PEIS-Ecology, combines insights from the fields of autonomous robotics and ambient intelligence to provide a new approach to building robotic systems in the service of people. In this paper, we present this vision, and we report the results of a four-year collaborative research project between Sweden and Korea aimed at the concrete realization of this vision. We focus in particular on three results: a robotic middleware able to cope with highly heterogeneous systems; a technique for autonomous self-configuration and reconfiguration; and a study of the problem of sharing information of both physical and digital nature.
Abstract-The fields of autonomous robotics and ambient intelligence are converging toward the vision of smart robotic environments, in which tasks are performed via the cooperation of many networked robotic devices. To enable this vision, we need a common communication and cooperation model that can be shared between robotic devices at different scales, ranging from standard mobile robots to tiny embedded devices. Unfortunately, today's robot middlewares are too heavy to run on tiny devices, and middlewares for embedded devices are too simple to support the cooperation models needed by an autonomous smart environment. In this paper, we propose a middleware model which allows the seamless integration of standard robots and simple off-the-shelf embedded devices. Our middleware is suitable for building truly ubiquitous robotics applications, in which devices of very different scales and capabilities can cooperate in a uniform way. We discuss the principles and implementation of our middleware, and show an experiment in which a mobile robot, a commercial mote, and a custom-built mote cooperate in a home service scenario.
Many sensor networks have lately included actuation as an important property of the nodes. With the introduction of actuation, new requirements are posed on these nodes in terms of reconfiguration of collaboration patterns. The sensors/actuators are very often connected to various heterogeneous hardware that have a few KBs of memory, low processing power and communication range, such as WSN motes. Also, for many applications networks of small and simple sensor and actuator nodes need to cooperate with networked robotic devices, which leads to further requirements to enable collaboration between devices of different scales. In this networked robot and sensor/actuator infrastructure, tasks are performed by the cooperation of multiple devices. Dynamically changing availability of devices as well as changes of tasks lead to a need of reconfiguration of the devices at runtime. Therefore a mechanism should be available in the communication level, which affords reconfiguration ability to the sensor/actuator nodes as well as robots. In this article, a concept called indirect reference is proposed, which facilitates dynamic reconfiguration of sets of distributed devices. We describe here also an implementation of the concept on a ubiquitous robotic middleware, which offers seamless integration of robots and WSN motes like tiny embedded devices with an example.
Abstract-Robotic middlewares increasingly allow the seamless integration of multiple heterogeneous robots into one distributed system. Unfortunately, very simple devices like tagged everyday objects and smart objects are left orphan in this otherwise pervasive trend. We claim that the inclusion of simple everyday objects as part of distributed robot systems would have many advantages, and propose a design pattern to allow this inclusion. We make this pattern concrete by describing an implementation of it using a specific multi-robot middleware, called PEIS-Ecology Middleware. We also show an illustrative experiment which integrates everyday objects in a smart home equipped with mobile robots as well as more advanced distributed sensor nodes.
Map building is a classical problem in mobile and autonomous robotics, and sensor models is a way to interpret raw sensory information, especially for building maps. In this paper we propose a parameterized sensor model, and optimize map goodness with respect to these parameters. A new approach, measuring the goodness of maps without a handcrafted map of the actual environment is introduced and evaluated. Three different techniques; statistical analysis, derivative of images, and comparison of binary maps have been used as estimates of map goodness. The results show that the proposed sensor model generates better maps than a standard sensor model. However, the proposed approach of measuring goodness of maps does not improve the results as much as expected.
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