We describe an approach for integrating ontologies with a constraint-based planner to compile configuration planning domains based on the current context. We consider two alternative approaches: The first one integrates SPARQL queries directly with the planner while the second one generates SPARQL queries dynamically from provided triples. The first approach offers the full freedom of the SPARQL query language, while the second offers a more dynamic way for the planner to influence queries based on what is currently relevant for the planner. We evaluate the approach based on how much redundancy is removed by “outsourcing” knowledge into the ontology compared to modeling it directly into the domain of the planner.

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count the number of users in the environment is important in order to accurately recognize the activities of (groups of) agents. For smart environments without cameras, the problem of counting the number of agents is non-trivial. This is in part due to the difficulty of using a single non-vision based sensors to discriminate between one or several persons, and thus information from several sensors must be combined in order to reason about the presence of several agents. In this paper we address the problem of counting the number of agents in a topologically known environment using simple sensors that can indicate anonymous human presence. To do so, we connect an ontology to a probabilistic model (a Hidden Markov Model) in order to estimate the number of agents in each section of the environment. We evaluate our methods on a smart home setup where a number of motion and pressure sensors are distributed in various rooms of the home.

Planners for real robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach to hybrid task and motion planning, in which state-based forward-chaining task planning is tightly coupled with motion planning and other forms of geometric reasoning. Our approach is centered around the problem of geometric backtracking that arises in hybrid task and motion planning: in order to satisfy the geometric preconditions of the current action, a planner may need to reconsider geometric choices, such as grasps and poses, that were made for previous actions. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the large size of the space of geometric states. We explore two avenues to deal with this issue: the use of heuristics based on different geometric conditions to guide the search, and the use of geometric constraints to prune the search space. We empirically evaluate these different approaches, and demonstrate that they improve the performance of hybrid task and motion planning. We demonstrate our hybrid planning approach in two domains: a real, humanoid robotic platform, the DLR Justin robot, performing object manipulation tasks; and a simulated autonomous forklift operating in a warehouse.

This paper presents the motivation and challenges to developingsemantic interoperability for an internet of things network that isused in the context of home based care. The paper describes a researchenvironment which examines these challenges and illustrates the motivationthrough a scenario whereby a network of devices in the home isused to provide high-level information about elderly patients by leveragingfrom techniques in context awareness, automated reasoning, andconguration planning.

In 2015, Örebro University was granted the rights to provide a Master of Science in Engineering (5 year engineering programme). This fall we launch two programmes, in Computer Science and Industrial Economics. The basis of these programmes rests on a pedagogical approach of increasing interest, namely CDIO (Conceive-Design-Implement-Operate), which aims to provide a framework particularly suited for technical and engineering programmes.

A number of non-trivial challenges were addressed when crafting a programme that conforms to the CDIO standards and guidelines. In particular, one of the more difficult tasks is to ensure that a programme in its entirety satisfies both the CDIO goals and the learning outcomes in the Swedish Higher Education Ordinance in a coherent and meaningful way. A further challenge is to educate the entire teaching core to help them adopt the model, and to guide the staff in finding proper use of CDIO within each different subject.

This talk presents how these challenges were addressed in adopting CDIO during the application process and the initial implementation stages. It describes how a department in rapid development was able to anchor the concepts of the pedagogical model with its teachers and programme directors. In particular, we outline the tools and processes which were used in order to create familiarity and consensus with the teaching core responsible for the new education. The talk also describes the difficulties encountered in applying a single pedagogical model to an education, and outlines the iterative process taken in order to integrate CDIO in a new programme and within its various courses.

In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies.

Planning for robots in environments co-inhabited by humans entails handling exogenous events during plan execution. Such events require plans to be continuously adapted to ensure that they remain "human-aware", i.e., adherent to human preferences and needs. We use an approach whereby human-awareness is enforced through so-called interaction constraints. Interaction constraints are used to infer context and appropriate goals online. The current plan is modified at run time so as to achieve courses of action that are continuously human-aware. The approach is evaluated in a research facility environment in which we simulate multiple days of planning and execution.

Configuration planning for a distributed robotic system is the problem of how to configure the system over time in order to achieve some causal and/or information goals. A configuration plan specifies what components (sensor, actuator and computational devices), should be active at different times and how they should exchange information. However, not all plans that solve a given problem need to be equally good, and for that purpose it may be important to take preferences into account. In this paper we present an algorithm for configuration planning that incorporates general partially ordered preferences. The planner supports multiple preference categories, and hence it solves a multiple-objective optimization problem: for a given problem, it finds all possible valid, non-dominated configuration plans. The planner has been able to successfully cope with partial ordering relations between quantitative preferences in practically acceptable times, as shown in the empirical results. Preferences here are represented as c-semirings, and are used for establishing dominance of a solution over another in order to obtain a set of configuration plans that will constitute the solution of a configuration planning problem with partially ordered preferences. The dominance operators tested in this paper are Pareto and Lorenz dominance. Our solver considers one guiding heuristic for obtaining the first solution, and then switches to a dominance based monotonically decreasing heuristic used for pruning dominated partial configuration plans. In our empirical results, we perform a statistical study in the space of problem instances and establish families of problems for which our approach is computationally feasible.

This paper gives a review of the recent developments in deep learning and unsupervised feature learning for time-series problems. While these techniques have shown promise for modeling static data, such as computer vision, applying them to time-series data is gaining increasing attention. This paper overviews the particular challenges present in time-series data and provides a review of the works that have either applied time-series data to unsupervised feature learning algorithms or alternatively have contributed to modifications of feature learning algorithms to take into account the challenges present in time-series data.