An embedded particle filter SLAM implementation using an affordable platform

PostprintThe recent growth in robotics applications has put to evidence the need for autonomous robots. In order for a robot to be truly autonomous, it must be able to solve the navigation problem. This paper highlights the main features of a fully embedded particle filter SLAM system and introduces some novel ways of calculating a measurement likelihood. A genetic algorithm calibration approach is used to prevent parameter over-fitting and obtain more generalizable results. Finally, it is depicted how the developed SLAM system was used to autonomously perform a field covering task showing robustness and better performance than a reference system. Several lines of possible improvements to the present system are presented

Multi-Scale Spatial Cognition Models and Bio-Inspired Robot Navigation

The rodent navigation system has been the focus of study for over a century. Discoveries made lately have provided insight on the inner workings of this system. Since then, computational approaches have been used to test hypothesis, as well as to improve robotics navigation and learning by taking inspiration on the rodent navigation system. This dissertation focuses on the study of the multi-scale representation of the rat’s current location found in the rat hippocampus. It first introduces a model that uses these different scales in the Morris maze task to show their advantages. The generalization power of larger scales of representation are shown to allow for the learning of more coherent and complete policies faster. Based on this model, a robotics navigation learning system is presented and compared to an existing algorithm on the taxi driver problem. The algorithm outperforms a canonical Q-Learning algorithm, learning the task faster. It is also shown to work in a continuous environment, making it suitable for a real robotics application. A novel task is also introduced and modeled, with the aim of providing further insight to an ongoing discussion over the involvement of the temporal portion of the hippocampus in navigation. The model is able to reproduce the results obtained with real rats and generates a set of empirically verifiable predictions. Finally, a novel multi-query path planning system is introduced, inspired in the way rodents represent location, their way of storing a topological model of the environment and how they use it to plan future routes. The algorithm is able to improve the routes in the second run, without disrupting the robustness of the underlying navigation system

Preventer, A Selection Mechanism For Just-In-Time Preventive Interventions

This paper examines just-in-time adaptive interventions (JITAIs) for stress, a pervasive and affective computing application with significant implications for long-term health and quality of life. We discuss fundamental components needed to enabling JITAIs based for one kind of affect data stress. Chronic stress has significant long-term behavioral and physical health consequences, including an increased risk of cardiovascular disease, cancer, anxiety and depression. This paper conducts post-hoc experiments and simulations to demonstrate feasibility of both real-time stress forecasting and stress intervention adaptation and optimization. Using physiological data collected by ten individuals in the natural environment for one week, we show 1) that simple Hidden Markov Models (HMMs) can be used to forecast physiological measures of stress with up to 3 minutes in advance; and 2) Q-Learning (QL) combined with eligibility traces could be used by an affective computing system to adapt and deliver any number and type of interventions in response to changes in affect. Our hope is that this work will take us one step closer to using pervasive devices to assist in the daily management of chronic stress and other affect-related challenges

An embedded particle filter SLAM implementation using an affordable platform

PostprintThe recent growth in robotics applications has put to evidence the need for autonomous robots. In order for a robot to be truly autonomous, it must be able to solve the navigation problem. This paper highlights the main features of a fully embedded particle filter SLAM system and introduces some novel ways of calculating a measurement likelihood. A genetic algorithm calibration approach is used to prevent parameter over-fitting and obtain more generalizable results. Finally, it is depicted how the developed SLAM system was used to autonomously perform a field covering task showing robustness and better performance than a reference system. Several lines of possible improvements to the present system are presented