Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.

One of the most prevalent approaches among distributed architectures is Multi-Agent Systems. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing distributed systems based on highly dynamic scenarios. Nevertheless, the development of a multi-agent system can be an extensive and delicate process. During this process, it is convenient to utilize Agent-Oriented Software Engineering (AOSE) tools. Such tools facilitate and improve the engineering process, achieving models that are more detailed and closer to the multi-agent systems implementation. This paper presents the analysis and design of a Flexible and User Services Oriented Multi-agent Architecture (FUSION@). This is a new architecture that integrates intelligent agents with a service-oriented approach to facilitate and optimize the development of highly dynamic distributed systems.

Abstract. The main objective of Cloud Computing is to provide software, services and computing infrastructures carried out independently by the network. This concept is based on the development of dynamic, distributed and scalable software. In this way there are Service-Oriented Architectures (SOA) and agent frameworks which provide tools for developing distributed systems and multiagent systems that can be used for the establishment of cloud computing environments. This paper presents CISM@ (Cloud computing Integrated into Service-Oriented Multi-Agent) architecture set on top of the platforms and frameworks by adding new layers for integrating a SOA and Cloud Computing approach and facilitating the distribution and management of functionalities.

This paper presents a multiagent system that can process stereoscopic images and detect people with a stereo camera. In the first of two phases, the system creates a model of the environment using a disparity map. It can be constructed in real time, even if there are moving objects present in the area (such as people passing by). In the second phase, the system is able to detect people by combining a series of novel techniques. A multi-agent system (MAS) is used to deal with the problem. The system is based on cooperative and distributed mechanisms and was tested under different conditions and environments.I.

Abstract. Ambient Intelligence (AmI) based systems require the development of innovative solutions that integrate distributed intelligent systems with context-aware technologies. In this sense, Multi-Agent Systems (MAS) and Wireless Sensor Networks (WSN) are two key technologies for developing distributed systems based on AmI scenarios. This paper presents the new HERA (Hardware-Embedded Reactive Agents) platform, that allows using dynamic and self-adaptable heterogeneous WSNs on which agents are directly embedded on the wireless nodes This approach facilitates the inclusion of context-aware capabilities in AmI systems to gather data from their surrounding environments, achieving a higher level of ubiquitous and pervasive computing.