The successful manipulation of a robotic arm is crucial in industry, in surgical fields, or in space exploration. Many previous studies on evolutionary or genetic algorithms successfully determined effective configurations of arm joints in order to reach a specific target. Nevertheless, most existing methods face challenges in obstacle avoidance, as it might be required to find an extremely specific and not direct result to surpass a certain obstacle, causing local maxima to be found. This paper proposes the use of a known path-finding method such as the A* Search algorithm establish an optimized route for the arm to extend over, before the evolutionary algorithm is applied to determine the configurations of arm joints. Specifically, the process starts with a predefined scenario containing both the target, the starting point and a set of obstacles. The A* algorithm is then used to map a route, which is divided into a set of targets that the robotic arm must follow to reach its final destination. The evaluation function used in this project ensures that proximity to obstacles negatively affects individual fitness. The experimental findings show that this strategy is able to find a way around local maxima quickly and outperform previous purely evolutionary approaches in both 2D and 3D simulated environments.

The present paper evaluates some strategies to apply path re-planning algorithms for emergency landing of Unmanned Aerial Vehicles (UAVs). The strategies proposed are based on executing only one path re-planning algorithm or a combination of them using an ensemble approach. The proposed methods are integrated to the security supervision system of the UAV, namely In-Fly Awareness (IFA), where decision-making algorithms are applying after a critical situation happens. The critical situations considered are battery overheating and engine failure. The performance of the proposed strategies for emergency landing is evaluated in a real world scenario using the Software-In-The-Loop (SITL) technique. Computational results reported show that these strategies are promising based on the success rate to land the UAV safely.

This article describes the implementation of a strategy that selects, destroys, and replaces some individuals of a population of six real autonomous mobile robots. This strategy was called Predation. We introduce Predation as a methodology for improving the performance of an embedded evolutionary system developed for the automatic design of robotic controllers. The paper describes how the evolutionary system controls such a small robot population in Teal time and the effects of predation in avoiding local optimum. It is able to achieve obstacle avoidance behaviour with the robot population evolving while deployed in the field, instead of just using the evolving group to develop an optimum controller for a single robot.

This paper describes a fully embedded distributed evolutionary system that is able to achieve collision-free navigation in a few hundreds of trials. It reports the first experimental proof of the embedded evolution concept applied to the evolution of morphology and an unstructured control circuit of a population of six real robots in real time. Evolution selects the appropriate sensor configuration and speed levels that the robots should incorporate to perform specific tasks. The results show the influence of different mutation rates on the performance of the system. This work produced a genetic system where the population exists in a real environment.

This work aims to present the most relevant results derived from the development of a novel FPGA matrix. Nevertheless its capacity, its highlights are low cost and its ability to deal with high frequencies. This prototype matrix, named FLECHA, presents some structural novelties, as a different approach to perform the interconnections between logic cells and I/O pads and an internal controller. One of the major contributions of this work deals with the strategy of placing the logic cells in rows, allowing a drastic reduction of the number of switches without reducing interconnection capacity between logic cells and I/O pads. Following this principle, a new matrix presenting 600 equivalent gates was constructed including 40 logic cells and 40 programmable I/O pads. The FLECHA matrix is intended to implement simple logic functions as for fast "glue logic" between processors and memories, reaching an operational frequency of l45 MHz.