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This chapter reports an overview of the experience and the results achieved during the development of the robotized system for switchgear wiring carried out in the WIRES experiment. This specific application is particularly challenging for a robotic system due to the complexity of the manipulation task. As a matter of fact, in this task deformable linear objects, such as electric wires, are involved. Moreover, the precision requested during the assembly task and the typical crowded space inside the switchgear imply, on one side, the development of specific hardware and software tools and, on the other side, high adaptability and flexibility of the robotic system. In the WIRES experiment, a software package to extract the wiring information and to generate the robot task sequence directly from the switchgear CAD files has been developed. Additionally, a computer vision system able to recognize the location of the wires and of the electromechanical components inside the switchgear has been developed. To deal with the wire deformability and occlusion problems during the wire insertion, machine learning and sensor fusion techniques have been adopted to enable the wire manipulation by means of tactile sensors and 2D cameras feedback. From the hardware point of view, a specific end effector has been developed to manipulate and connect the wire to the components. This end effector is equipped with an electric screwdriver and a customized tactile sensor used to evaluate the wire shape, the wire end pose and its interaction with the environment during the manipulation. The entire task pipeline, going from the switchgear information extraction, to the wire grasp and manipulation, its connection and the routing along the desired wire path is presented in this chapter. The preliminary experimental results show that the developed system can achieve a success rate of about 95% in the wire insertion and connection task.

Modern surveillance systems, such as those based on the use of Unmanned Aerial Vehicles, require powerful high- performance platforms to deal with many different algorithms that make use of massive calculations. At the same time, low- cost and high-performance specific hardware (e.g., GPU, PPU) are rising and the CPUs turned to multiple cores, characteriz- ing together an interesting and powerful heterogeneous execu- tion platform. Therefore, reconfigurable computing is a poten- tial paradigm for those scenarios as it can provide flexibility to explore the computational resources on heterogeneous cluster attached to a high-performance computer system platform. As the first step towards a run-time reconfigurable workload bal- ancing framework targeting that kind of platform, application time requirements and its crosscutting behavior play an impor- tant role for task allocation decisions. This paper presents a strategy to reallocate specific tasks in a surveillance system composed by a fleet of Unmanned Aerial Vehicles using aspect- oriented paradigms in order to address non-functional applica- tion timing constraints in the design phase. An aspect support from a framework called DERAF is used to support reconfigu- ration requirements and provide the resource information needed by the reconfigurable load-balancing strategy. Finally, for the case study, a special attention on Radar Image Process- ing will be given.