An ambient intelligence approach for learning in smart robotic environments

Abstract

Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. To reduce the amount of preparation and preprogramming required for their deployment in real‐world applications, it is important to make these systems self‐adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre‐existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to proactively and smoothly adapt to subtle changes in the environment and in the habits and preferences of their user(s), in presence of appropriately defined performance measuring functions.

Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things

“…IoRT-based monitored and mobile edge computing environments [37][38][39][40] integrate sensor-based communication networks and fog computing technologies. Robotic operating and autonomous control systems harness sensor-actuator networks, machine learning techniques, and context recognition tools in relation to predictive performance of navigation and mapping tasks.…”

“…IoRT-based monitored and mobile edge computing environments integrate sensor-based communication networks and fog computing technologies. [33][34][35][36][37][38][39][40] Blockchain-based IoRT systems and networks leverage cloud and computing technologies, data mining and remote sensing tools, and deep and machine learning algorithms in visual object detection and recognition. Mobile robot fleets collect data from unknown environments using actuated devices and motion control algorithms.…”

“…Robotic knowledge sharing and computation task cooperation [33][34][35][36] require real-time complex data processing, motion coordination, and smart sensor devices. Image recognition algorithms and environment perception sensors [37][38][39][40] assist mobile autonomous robots in dynamic manufacturing environments.…”

Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

“…The availability of data from multimodal sources within a smart robotic environment [ 22 ] may help designing more robust methods for activity recognition. For instance, although recent advances on DL approaches have made video-based activity recognition a very powerful approach [ 23 ], this modality of data may be unavailable due to privacy restrictions, or it may be compromised by technical issues such as occlusions.…”

A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition

“…For inertial data, similar architectures have been proposed, processing either raw data [35,10] or descriptors obtained through feature extraction methods [47,1]. To provide a wider range of possibilities, robots may act symbiotically with other pervasive devices, such as wearable technologies or ambient sensors in intelligent environments, which may provide additional capabilities for sensing and acting based on application-specific components [2]. When synchronised data from different sensors are available, activity recognition techniques may rely on multiple sensor modalities to provide more accurate results, giving rise to techniques for multimodal activity recognition [28,17,43].…”