Abstract-Technological advances in recent years lead to the miniaturization of a whole arsenal of different sensors. They can be used to offer new services in eHealth applications, smart homes, robotics or smart cities. With the increasing diversity and the cooperation needed between these sensors in order to provide the best possible services to the user the systems that use the data coming from these sensors need to be able to handle conflicting information and thus also conflicting actions. In this paper we propose an approach that uses Hidden Markov Models in a first step to analyse the incoming data and in a second step uses a rule engine in order to handle the occurring conflicts.
Robots traditionally have a wide array of sensors that allow them to react to the environment and make appropriate decisions. These sensors can give incorrect or imprecise data due to malfunctioning or noise. Sensor fusion methods try to overcome some of these issues by using the data coming from different sensors and combining it. However, they often don't take sensor malfunctioning and a priori knowledge about the sensors and the environment into account, which can produce conflicting information for the robot to work with. In this paper, we present an architecture and process in order to overcome some of these limitations based on a proactive rule-based system.
Robots have to be able to function in a multitude of different situations and environments. To help them achieve this, they are usually equipped with a large set of sensors whose data will be used in order to make decisions. However, the sensors can malfunction, be influenced by noise or simply be imprecise. Existing sensor fusion techniques can be used in order to overcome some of these problems, but we believe that data can be improved further by computing context information and using a proactive rule-based system to detect potentially conflicting data coming from different sensors. In this paper we will present the architecture and scenarios for a generic model taking context into account.
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