In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the multiple methodologies that have been proposed to integrate planning and learning in the context of games. In order to provide a richer contextualization, the paper also presents learning and planning techniques commonly used in games, both in terms of their theoretical foundations and applications.

This paper presents a fast scan matching approach to mobile robot localization supported by a continuous likelihood field. The likelihood field plays a central role in the approach, as it avoids the necessity to establish direct correspondences; it is the connection link between scan matching and robotic localization, and it provides a reduced computational complexity. Scan matching is formulated as a non-linear least squares problem and solved by the Gauss-Newton and Levenberg-Marquardt methods. Furthermore, to reduce the influences of outliers during optimization, a loss function is introduced. The proposed solution was evaluated using a publicly available dataset and compared with AMCL, a state-of-the-art localization algorithm. Our proposal shows to be a fast and accurate localization algorithm suitable for any type of operation.

The adoption of robotics technology has the potential to advance quality, efficiency and safety for manufacturing enterprises, in particular small and medium-sized enterprises. This paper presents a human-robot interaction (HRI) system that enables a robot to receive commands, provide information to a human teammate and ask them a favor. In order to build a robust HRI system based on gesture recognition, three key issues are addressed: richness, multiple feature fusion and failure verification. The developed system has been tested and validated in a realistic lab with a real mobile manipulator and a human teammate to solve a puzzle game.

Bipedal humanoid robots have complex dynamics and they are intrinsically unstable. Although, bipedal robots have a similar kinematic architecture to a human and they are the most appropriate type of robots to operate in human environments, developing a humanoid robot that has robust is a difficult task. This paper proposes an omnidirectional walking engine that takes into account the push recovery strategies. The walking engine has a hierarchical structure and tries to fade the complexities of the dynamic walking. In addition, it can adapt to another platform with few changes. We enhanced the Linear Inverted Pendulum Plus Flywheel Model to release the height constraint of the center of mass. This enhancement allows a more human-like motion and provides more stable walking. The proposed walking engine has been successfully tested on a real humanoid soccer robot. The experimental results show the performance of this controller to generate stable walking. The average speed of walking that we have achieved was 20cm/sec.

The Smart City concept is starting to extend into maritime environments alongside with the increase of Unmanned Surface Vehicles (USV) models on the market. Consequently, by joining both Smart City and USV technologies, a set of platforms and applications for aquatic environments are emerging. This work proposes a low-cost aquatic mobile sensing platform for data gathering with a swarm of USVs communicating through a Delay-Tolerant Network (DTN). A set of DTN link quality-based routing strategies select the best quality path in a dynamic approach so the sensed information is able to reach the mobile gateway in a reliable way. A Link Quality Estimation (LQE) approach is proposed and its accuracy is evaluated through real experimentation. An aquatic simulation environment, considering both navigation and communication layers, was also proposed and used to evaluate the performance of the proposed routing strategies, and complement real environment performance studies.