Abstract
The main objective of this project was to evaluate the sensing system for person detection in the scope of its integration in a mobile robotic platform for Ambient Assisted Living. Two sensors were considered, a camera and a depth sensor. For the camera (2D), 3 different algorithms were implemented: face detection, tracking and recognition. For the depth sensor the whole body detection was tested using skeleton tracking. The results showed that the face detection and recognition algorithms had a very small range, and the face tracking demonstrated to reach a higher distance. However, the latter exhibited poor results when confronted with illumination variations. The skeleton tracking algorithm provided good results, being capable of tracking relatively close to the sensor and up to 3 meters distance. Thus, the 2D face recognition can be used for short distances to identify the person, while the 3D skeleton tracking can be appropriated for distant tracking of the person. This work showed that the integration of these sensing systems, in a robotic platform, can make a robust robot capable of human interaction in home environments. © 2013 IEEE.

Abstract
In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution for the perception task considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to improve the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic dominates the common heuristic (based on the euclidian distance), and present the results of the performance increase in terms of node expansion.

Abstract
Maximizing the performance of cooperative perception of a tracked target by a team of mobile robots while maintaining the team's formation is the core problem addressed in this work. We propose a solution by integrating the controller and the estimator modules in a formation control loop. The controller module is a distributed non-linear model predictive controller and the estimator module is based on a particle filter for cooperative target tracking. A formal description of the integration followed by simulation and real robot results on two different teams of homogeneous robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target's cooperative estimate while complying with the performance criteria such as keeping a pre-set distance between the team-mates and/or the target and obstacle avoidance.

Abstract
This paper deals with the problem of active target tracking with obstacle avoidance for multi-robot systems. A nonlinear model predictive formation control is presented which uses potential functions as terms of the cost function. These terms penalize the proximity with mates and obstacles, splitting the problem of obstacle avoidance into two repulse functions. Experimental results with real robots are presented to demonstrate the performance of the approach. © IFAC.

Abstract
The field of Robotics has become one of the most rapidly growing fields in the research and technological world. The development of flexible robots represents the possibility of them becoming a highly efficient operator in the industrial environment. Intelligent robots present key characteristics that enable the streamlining of automated processes associated to industry. Adding the adaptive component to such robots facilitates the design of solutions for a wide range of problems. Pick and Place operations have attracted considerable interest from the research and industrial community as they present one of the most effective solutions to typical problems such as handling or transportation. Another aspect to consider when developing a robotic solution for pick and place approaches is the methodology for recognition of the objects to be handled. In this paper, it will be presented a methodology that can be applicable to different scenarios in order to both identify the objects of a given scene and reply to the need of handling those objects. Furthermore it will be presented one specific case study that used the proposed methodology, the Amazon Picking Challenge - a challenge aiming to develop solutions for the complete automation of a dispatching warehouse. Our proposed methodology was built using the Robotic Operative System (ROS) framework and is based in three tiers: recognition, movement / actuation and control. ROS allows the decomplexation of typical problems associated to robotics as this framework promotes the development of modular and simple software that together fulfill the state-of-art requests of the industry. Since ROS is becoming an important tool in robotics, using a methodology developed in ROS allows for the development of a standard approach to pick and place operations. Another advantage of our methodology is the ability to have a robot safely and efficiently inserted in an unknown environment. This is possible due to adaptive control tier. Proposed improvements to currently available methods will be also described in this project. Throughout the document, the importance of this project and the development of novel robots will be described taken into consideration the need for robots in the industrial setting.

Abstract
This paper introduces a new approach to 3-D position estimation from acceleration data, i.e., a 3-D motion tracking system having a small size and low-cost magnetic and inertial measurement unit (MIMU) composed by both a digital compass and a gyroscope as interaction technology. A major challenge is to minimize the error caused by the process of double integration of accelerations due to motion (these ones have to be separated from the accelerations due to gravity). Owing to drift error, position estimation cannot be performed with adequate accuracy for periods longer than few seconds. For this reason, we propose a method to detect motion stops and only integrate accelerations in moments of effective hand motion during the demonstration process. The proposed system is validated and evaluated with experiments reporting a common daily life pick-and-place task.