A deep reinforcement learning approach for active SLAM

In this paper, we formulate the active SLAM paradigm in terms of model-free Deep Reinforcement Learning, embedding the traditional utility functions based on the Theory of Optimal Experimental Design in rewards, and therefore relaxing the intensive computations of classical approaches. We validate such formulation in a complex simulation environment, using a state-of-the-art deep Q-learning architecture with laser measurements as network inputs. Trained agents become capable not only to learn a policy to navigate and explore in the absence of an environment model but also to transfer their knowledge to previously unseen maps, which is a key requirement in robotic exploration

GestureMoRo: an algorithm for autonomous mobile robot teleoperation based on gesture recognition

Gestures are a common way people communicate. Gesture-based teleoperation control systems tend to be simple to operate and suitable for most people’s daily use. This paper employed a LeapMotion sensor to develop a mobile robot control system based on gesture recognition, which mainly established connections through a client/server structure. The principles of gesture recognition in the system were studied and the relevant self-investigated algorithms—GestureMoRo, for the association between gestures and mobile robots were designed. Moreover, in order to avoid the unstably fluctuated movement of the mobile robot caused by palm shaking, the Gaussian filter algorithm was used to smooth and denoise the collected gesture data, which effectively improved the robustness and stability of the mobile robot’s locomotion. Finally, the teleoperation control strategy of the gesture to the WATER2 mobile robot was realized, and the effectiveness and practicability of the designed system were verified through multiple experiments

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Estrategias de Deep Learning en SLAM Activo

El SLAM (Simultanous Localisation and Mapping) activo hace referencia al problema de controlar el movimiento de un robot que está realizando SLAM, de forma que se minimice la incertidumbre del mapa creado y de su localización. Tradicionalmente ha sido resuelto mediante filtros u otras aproximaciones que involucran procesos de decisión de Markov o algoritmos de aprendizaje por refuerzo. En éstos, es necesario (i) identificar las posibles acciones, (ii) calcular el valor futuro esperado de cada una de ellas (e.g. mediante funciones de utilidad) y (iii) ejecutar la acción óptima. En este Trabajo Fin de Máster se analiza la resolución del problema mediante redes neuronales profundas, un campo de gran auge en la actualidad donde el aprendizaje por excelencia es el supervisado, que atrae la mayoría de investigaciones y aplicaciones de la literatura. La naturaleza del problema abordado, sin embargo, hace necesario el uso de otra forma de aprendizaje automático: el aprendizaje por refuerzo profundo. Se ha analizado el potencial y las limitaciones de este marco de trabajo, empleado normalmente en entornos de simulación sencillos, donde la diferencia entre exploración y navegación y el problema de generalización (clave en el SLAM activo, puesto que la información a priori del entorno es nula) son habitualmente obviados. Se han implementado distintas aproximaciones de aprendizaje por refuerzo y refuerzo profundo basadas en Q-learning sobre el entorno de simulación Gazebo. Ambos aprendizajes y su capacidad de generalización a escenarios desconocidos se estudian en profundidad, consiguiendo que agentes entrenados naveguen por entornos totalmente desconocidos. Además, se propone la inclusión de una métrica de la matriz de covarianza en la función de recompensa, consiguiendo una reducción de entropía paulatina durante la exploración y favoreciendo acciones mucho más óptimas en términos de reducción de la in- certidumbre.<br /