Network robot systems (NRS) provide many scientific and technological challenges, given that robots interact with each other as well as with sensors present in the environment to accomplish certain tasks. In this work, we consider an essential problem in NRS, namely how to perform task planning given the limitations both in on-board sensing as well as in the environment's sensors. Partially observable Markov decisions processes (POMDPs) form an attractive framework to address planning in the uncertain environments that typify NRS. We show how to model a typical cooperative perception task in a NRS, namely tracking and classifying people, and we present experiments that show how the proposed approach results in an effective interplay between robot and environment sensors.

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.

In this work we study the efficient search of objects in domestic environments, using probabilistic logic to represent uncertainty about object location and partially observable Markov decision processes (POMDP) for the decision-making process regarding the movements to be carried out by the robot to improve its belief about the object locations. We propose the use of a semantic map that stores information about the knowledge in the system and updates it, by an inference process, with sensor information received from the object recognition module. However, semantic maps are not capable of actively search for more information in the environment. For that reason a decision-making module, based on a POMDP framework, is integrated in the system. Several experiments were made in a realistic apartment test bed using every day objects and a mobile robot, showing that this hybrid solution makes the search process more efficient.

In traditional decision-theoretic planning, information gathering is a means to a goal. The agent receives information about its environment (state or observation) and uses it as a way to optimize a state-based reward function. Recent works, however, have focused on application domains in which information gathering is not only the mean but the goal itself. The agent must optimize its knowledge of the environment. However, traditional Markov-based decision-theoretic models cannot account for rewarding the agent based on its knowledge, which leads to the development of many approaches to overcome this limitation. We survey recent approaches for using decision-theoretic models in information-gathering scenarios, highlighting common practices and existing generic models, and show that existing methods can be categorized into three classes: reactive sensing , single-agent active sensing , and multi-agent active sensing . Finally, we highlight potential research gaps and suggest directions for future research.