Abstract: The target of this research project was a feasibility study for the development of a robot for automatic or semi-automatic artichoke harvesting. During this project, different solutions for the mechanical parts of the machine, its control system and the harvesting tools were investigated. Moreover, in cooperation with the department DISPA of University of Catania, different field structures with different kinds of artichoke cultivars were studied and tested. The results of this research could improve artichoke production for preserves industries. As a first step, an investigation on existing machines has been done. From this research, it has been shown that very few machines exist for this purpose. Based also on previous experiences, some proposals for different robotic systems have been done, while the mobile platform itself was developed within another research project. At the current stage, several different configurations of machines and harvesting end-effectors have been designed and simulated using a 3D CAD environment interfaced with Matlab®. Moreover, as support for one of the proposed machines, an artificial vision algorithm has been developed in order to locate the artichokes on the plant, with respect to the robot, using images taken with a standard webcam.
Abstract: Testing agricultural operations and management practices associated with different machinery, systems and planning approaches can be both costly and time-consuming. Computer simulations of such systems are used for development and testing; however, to gain the experience of real-world performance, an intermediate step between simulation and full-scale testing should be included. In this paper, a potential common framework using the LEGO Mindstorms NXT micro-tractor platform is described in terms of its hardware and software components. The performance of the platform is demonstrated and tested in terms of its capability of supporting decision making on infield operation planning. The proposed system represents the basic measures for developing a complete test platform for field operations, where route plans, mission plans, multiple-machinery cooperation strategies and machinery coordination can be executed and tested in the laboratory.
Abstract: Endometrial cancer is the most common gynecological cancer in women in most of the developed world. The majority of these women with endometrial cancer will be unaffected by their disease. The challenge therefore is for surgical treatment not to be worse than the disease. Robotics has changed the way that we care for women living with endometrial cancer by making low-impact surgical treatment available to more women than was previously possible.
Abstract: In this paper, an adaptive human-machine interaction (HMI) method that is based on surface electromyography (sEMG) signals is proposed for the hands-free control of an intelligent wheelchair. sEMG signals generated by the facial movements are obtained by a convenient dry electrodes sensing device. After the signals features are extracted from the autoregressive model, control data samples are updated and trained by an incremental online learning algorithm in real-time. Experimental results show that the proposed method can significantly improve the classification accuracy and training speed. Moreover, this method can effectively reduce the influence of muscle fatigue during a long time operation of sEMG-based HMI.
Abstract: The research on intelligent robots will produce robots that are able to operate in everyday life environments, to adapt their program according to environment changes, and to cooperate with other team members and humans. Operating in human environments, robots need to process, in real time, a large amount of sensory data—such as vision, laser, microphone—in order to determine the best action. Intelligent algorithms have been successfully applied to link complex sensory data to robot action. This editorial briefly summarizes recent findings in the field of intelligent robots as described in the articles published in this special issue.
Abstract: Swarming and modular robotic locomotion are two disconnected behaviours that a group of small homogeneous robots can be used to achieve. The use of these two behaviours is a popular subject in robotics research involving search, rescue and exploration. However, they are rarely addressed as two behaviours that can coexist within a single robotic system. Here, we present a bio-inspired decision mechanism, which provides a convenient way for evolution to configure the conditions and timing of behaving as a swarm or a modular robot in an exploration scenario. The decision mechanism switches among two behaviours that are previously developed (a pheromone-based swarm control and a sinusoidal rectilinear modular robot movement). We use Genetic Programming (GP) to evolve the controller for these decisions, which acts without a centralized mechanism and with limited inter-robot communication. The results show that the proposed bio-inspired decision mechanism provides an evolvable medium for the GP to utilize in evolving an effective decision-making mechanism.