Abstract
The scientific and technological development, together with the world of robotics, is constantly evolving, driven by the need to find new solutions and by the ambition of human beings to develop systems with increasingly efficiency. Consequently, it is necessary to develop planning algorithms capable of effectively and safely move a robot within a given non structured scene. Moreover, despite of the several robotic solutions available, there are still challenges to standardise a development technique able to obviate some pitfalls and limitations present in the robotic world. The Robotic Operative System (ROS) arise as the obvious solution in this regard. Throughout this project it was developed and implemented a double A* path planning methodology for automatic manipulators in the industrial environment. In this paper, it will be presented an approach with enough flexibility to be potentially applicable to different handling scenarios normally found in industrial environment.

Abstract
This paper describes a robot with 12 degrees of freedom for pick-and-place operations using bricks. In addition, an optimization approach is proposed, which determines the state of each joint (that establishes the pose for the robot) based on the target position while minimizing the effort of the servomotors avoiding the inverse kinematics problem, which is a hard task for a 12 DOF robot manipulator. Therefore, it is a multi-objective optimization problem that will be solved using two optimization methods: the Stretched Simulated Annealing method and the NSGA II method. The experiments conducted in a simulation environment prove that the proposed approach is able to determine a solution for the inverse kinematics problem. A real robot formed by several servomotors and a gripper is also presented in this research for validating the solutions.

Abstract
This paper describes an optimization procedure for a robot with 12 degrees of freedom avoiding the inverse kinematics problem, which is a hard task for this type of robot manipulator. This robot can be used to pick and place tasks in complex designs. Combining an accurate and fast direct kinematics model with optimization strategies, it is possible to achieve the joints angles for a desired end-effector position and orientation. The optimization methods stretched simulated annealing algorithm and genetic algorithm were used. The solutions found were validated using data originated by a real and by a simulated robot formed by 12 servomotors with a gripper.

Abstract
Flexible and self-adaptive behaviours in automated quality control systems are features that may significantly enhance the robustness, efficiency and flexibility of the industrial production processes. However, most current approaches on automated quality control are based on rigid inspection methods and are not capable of accommodating to disturbances affecting the image acquisition quality, fact that hast direct consequences on the system's reliability and performance. In an effort to address the problem, this paper presents the development of a self-adaptive software system designed for the pre-processing (quality enhancement) of digital images captured in industrial production lines. The approach introduces the use of scene recognition as a key-feature to allow the execution of customized image pre-processing strategies, increase the system's flexibility and enable self-adapting conducts. Real images captured in a washing machines production line are presented to test and validate the system performance. Experimental results demonstrate significant image quality enhancements and a valuable reliability improvement of the automated quality control procedures.

Abstract
The robotic football competition has encouraged the participants to develop new ways of solving different problems in order to succeed in the competition. This article shows a different approach to the ball detection and recognition by the robot using a Kinect System. It has enhanced the capabilities of the depth camera in detecting and recognizing the ball during the football match. This is important because it is possible to avoid the noise that the RGB cameras are subject to for example lighting issues.

Abstract
Develop cost-effective ground robots for crop monitoring in steep slope vineyards is a complex challenge. The terrain presents harsh conditions for mobile robots and most of the time there is no one available to give support to the robots. So, a fully autonomous steep-slope robot requires a robust automatic recharging system. This work proposes a multilevel system that monitors a vineyard robot autonomy, to plan off-line the trajectory to the nearest recharging point and dock the robot on that recharging point considering visual tags. The proposed system called VineRecharge was developed to be deployed into a cost-effective robot with low computational power. Besides, this paper benchmarks several visual tags and detectors and integrates the best one into the VineRecharge system. © Springer International Publishing AG 2018.