Online unsupervised occupancy anticipation system applied to residential heat load management

Human preferences and lifestyles significantly impact buildings' energy consumption. Consequently, a better understanding of occupants' behavior is crucial to decrease energy consumption and maintain occupants' comfort. Occupant-centric control (OCC) strategies are effective approaches to fulfil such a purpose. As such, occupancy detection and prediction are of prime importance, particularly to manage Electric Space Heating (ESH) systems, due to the relatively slow dynamics of the temperature in dwellings. This paper proposes an Explicit Duration Hidden Markov Model (EDHMM) for unsupervised online presence detection and a hazard-based approach for occupancy prediction. Moreover, a control strategy using a cost function, weighted by occupancy predictions, and a load-shifting strategy based on time-varying electricity price are put forward. This work initially validates the consistency of the proposed approach by using synthetic data generated by a Monte Carlo simulation. Subsequently, the performance of our framework is compared with previous methods presented in the literature through experimental validation. Results demonstrate that the proposed EDHMM approach is efficient in detecting occupancy states. Besides, the results of the field implementation show the potential of the proposed control strategy to preserve occupants' thermal comfort while decreasing the heating energy consumption

HMM-Based Dynamic Mapping with Gaussian Random Fields

This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments

Survey of maps of dynamics for mobile robots

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area

Mapping in uncertain environments for mobile robots

Um dos problemas fundamentais em robótica móvel é o problema da localização e mapeamento, no qual um robô se deve localizar ao mesmo tempo que constrói um mapa do ambiente. Existem diversas técnicas para abordar este problema. Neste trabalho propõem-se abordagens novas para a construção do mapa em ambientes estáticos e dinâmicos, assumindo pose conhecida. As abordagens propostas baseiam-se em campos aleatórios de Markov (Markov random fields - MRF) e em campos aleatórios Gaussianos (Gaussian random fields - GRF), seguindo um ponto de vista Bayesiano, onde as distribuições de probabilidade a priori são usadas como regularizadores. Num ambiente estático, cada ponto do espaço é descrito pela sua probabilidade de ocupação. O primeiro método proposto é um filtro baseado nos MRF, que se centra no ruído das medidas e que pode ser implementado em linha (tempo real). O segundo método é um método preditivo baseado nos MRF que permite também estimar a probabilidade de ocupação do espaço não observado. Em ambos os métodos, os mapas são construídos numa grelha de células. Outra abordagem baseia-se num espaço contínuo, baseado em GRF onde se propõe um método recursivo de modo a reduzir a complexidade computacional. No caso de ambientes dinâmicos, a probabilidade de ocupação é substituída pelas probabilidade de transição duma cadeia de Markov para descrever o comportamento dinâmico de cada ponto. Nesta abordagem são propostos dois métodos para os ambientes dinâmicos, igualmente baseados nos MRF e nos GRF. No método com MRF todos os parâmetros são estimados em conjunto. Pelo contrário, com os GRF os parâmetros são divididos em dois sub-conjuntos de modo a reduzir a complexidade computacional. Todos os métodos propostos são testados e apresentam-se resultados em simulação nos respetivos capítulos. Finalmente estes algoritmos são também validados em ambiente experimental. Nestas experiências, as poses não podem ser medidas com precisão e é tida em consideração a incerteza na pose do robô. Quando comparados com o estado da arte, os métodos propostos resolvem as inconsistências nos mapas tendo em consideração a dependência entre pontos vizinhos. Este processo é realizado usando MRF e GRF em vez de assumir independência. As simulações e os resultados experimentais demonstram que os métodos propostos podem, não apenas lidar com as inconsistências nos mapas construídos, mas também tirar proveito da correlação espacial para prever o espaço não observado; Abstract: Mapping in Uncertain Environments for Mobile Robots One of the fundamental problems in robotics is the localization and mapping problem, where a robot has to localize itself while building a map of the environment. Several techniques exist to tackle this problem. This work proposes novel mapping approaches with known robot poses for static and dynamic environments. The proposed techniques are based on Markov random fields (MRFs) and Gaussian random fields (GRFs), following a Bayesian viewpoint where prior distributions are provided as regularizers. In static environments, every point is described by its occupancy probability. The first proposed method is an MRF-based filter, which focuses on the measurement noise and can be implemented online (realtime). The second one is an MRF-based prediction method, which can also be used to estimate the occupancy probability for unobserved space. In both methods, the maps are organized as a grid. Another approach, which works in continuous space, is based on a GRF prediction method, and a recursive algorithm is proposed to reduce the computational complexity. In the case of dynamic environments, the occupancy probability is replaced by transition probabilities of a Markov chain that describe the dynamic behaviour of each point. Two methods for dynamic environments are proposed, also based on MRFs and GRFs. In the MRF-based method, all the parameters are jointly estimated. In contrast, in the GRF-based method, the parameters are divided into two subsets to reduce the computational complexity. All the proposed methods are tested in simulations in the corresponding chapters. Finally, these algorithms are also validated on an experimental platform. In the experimental environments, robot poses cannot be measured precisely, and so the uncertainty of robot poses is also considered. When compared with the state of the art for dynamic environments, the proposed methods tackle the inconsistencies in the maps by considering dependence between neighbour points. This is done using MRFs and GRFs instead of assuming independence. The simulations and the experimental results demonstrate that the proposed methods can, not only deal with the inconsistency in the built maps, but also take advantage of the spatial correlation to predict unobserved space