In order to give assertive support, robots need to understand the cognitive and emotional characteristics of learners while in the learning process. Also, if the task involves handling capacity, robots must have similar skills. Three concepts were explored to control the cognitive and emotional robot’s behavior: the psychological flow theory, the scaffolding pedagogic strategy, and the multiagents-software paradigm. Based on these concepts, the Human-Robot Scaffolding architecture was designed. It is divided into five blocks. First, the sensory block recognizes body gestures, speech, and task state. Second, the beliefs block estimates the skills and emotional state of learners. Third, the desires block validates the goals the robot can reach; the goals are grouped in skills development, emotional control, cognitive control, challenge control, life signals, and immediate support. Fourth, the intentions block, based on the goals competition strategy, selects the goal that the robot will perform. Finally, the action-planner block regulates the robot’s movements according to the robot’s emotions. The validation procedure was done with 53 learners ranging between 10 and 13 years old who study in public and private schools. Based on the research achievements, the robot fosters learning the Mean-Ends Analysis strategy and the solution of a problem. A video fragment that summarizes the research process is available in https://youtu.be/qbohCjBIwYc .

One of the current challenges in information technology is to provide instant access to relevant information, without being limited by the location of the user or the device used to retrieve it. Current technologies allow users to access an overwhelming amount of data. This is an undesirable situation, since it is very difficult for users to obtain relevant information from these data. Enriching the user experience, which means, adding criteria such as user preferences and characteristics of his/her mobile context of use (e.g., location, access device features), can solve the aforementioned problem, since the information can be better adjusted to user needs and space-time situation, reducing the volume of information received by the user and avoiding information overloading. This paper presents an approach to enrich the user experience called Agents for Enrichment Services (AES), an adaptation framework based on agents that provides users with information tailored to their specific needs, features, devices, and context, with the purpose of giving relevant information at the right moment, place, and device.

Groupware applications, acquire more importance; within a mobile computing environment, users need to interact, to conform communities and to pursue common objectives without concerning about their individual context. This paper presents the Ayllu architecture, which is intended to develop groupware applications with a cooperative approach, called 5C paradigm. The fundamental idea is that cooperation between people must be carried out by explicit mechanisms supporting collaboration, coordination and conflict resolution, which are supported by intentional communication acts. A middleware based on this approach has been constructed. It uses software agents as mediators between users working cooperatively. Agents act together to support cooperative services by means of a mechanism, called volatile group. Agent groups are created dynamically, well-defined agent interaction protocols allow to provide the communication patterns that support people cooperation. The Ayllu architecture also includes means to deal with the inherent limitations of mobile devices.

The Backtracking Spiral Algorithm (BSA) is a coverage strategy for mobile robots based on the use of spiral filling paths; in order to assure the completeness, unvisited regions are marked and covered by backtracking mechanism. The BSA basic algorithm is designed to work in an environment modeled by a coarse-grain grid. BSA has been extended to cover, not only the free cells, but also the partially occupied ones. In this paper, the concepts and algorithms used to extend BSA are introduced. The ideas used to extend BSA are generic, thus a similar approach can be used to extend most of the grid-based coverage algorithms. Finally, some simulation results that demonstrate that BSA performs a complete coverage are presented.

This paper presents a new coverage online strategy BSA, backtracking spiral algorithm. Some service tasks involve the filling/sweeping of the surface that is accessible to the robot from its starting position. BSA is a structured way to perform this kind of procedures assuring the complete coverage of the surface regardless of the space topology. One of the main innovations of BSA is the use of spiral filling paths instead of zig-zag like paths, which makes it robust regardless of the robot initial orientation. In order to assure that the whole surface is covered, the algorithm is based in a backtracking procedure that makes the robot get back to unfulfilled detected regions. BSA is implemented with a set of simple rules. As the first ring of the spiral-filling path is accomplished nearby the obstacles, BSA indirectly contributes to improve the performance of a localisation system using obstacles as beacons. BSA has been validated in simulation.

This paper presents the concept of "complementary regions". It is the basis of our surface filling navigation strategy. It allows a mobile robot to: obtain a compact topological graph representation of the environment; solve the "terrain acquisition" problem; and plan point to point trajectories. This space decomposition concept is general and can be adapted to other robotics problems. The algorithm for a 2D surface region filling operation is introduced. It has been successfully tested using an autonomous mobile robot. Correctness and applicability are demonstrated its capability of exploring and navigating the entire accessible area of an a priori unknown indoor environment. A high level of performance has been reached.