SummarySteep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35°). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot’s center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.
The use of robotic palletizing systems has been increasing in the so-called Fast Moving Consumer Goods (FMCG) industry. However, because of the type of solutions developed, focused on high performance and efficiency, the degree of adaptability of packaging solutions from one type of product to another is extremely low. This is a relevant problem, since companies are changing their production processes from low variety / high volume to high variety / low volume. This environment requires companies to perform the setup of their robots more frequently, which has been leading to the need to use offline programming tools that decrease the required robot stop time. This work addresses these problems and, in this paper, is described the solution proposed for the automated offline development of collision free robot programs for palletizing applications.
Intraoperative evaluation of the efficacy of Deep Brain Stimulation includes evaluation of the effect on rigidity. A subjective semi-quantitative scale is used, dependent on the examiner perception and experience. A system was proposed previously, aiming to tackle this subjectivity, using quantitative data and providing real-time feedback of the computed rigidity reduction, hence supporting the physician decision. This system comprised of a gyroscope-based motion sensor in a textile band, placed in the patients hand, which communicated its measurements to a laptop. The latter computed a signal descriptor from the angular velocity of the hand during wrist flexion in DBS surgery. The first approach relied on using a general rigidity reduction model, regardless of the initial severity of the symptom. Thus, to enhance the performance of the previously presented system, we aimed to develop models for high and low baseline rigidity, according to the examiner assessment before any stimulation. This would allow a more patient-oriented approach. Additionally, usability was improved by having in situ processing in a smartphone, instead of a computer. Such system has shown to be reliable, presenting an accuracy of 82.0% and a mean error of 3.4%. Relatively to previous results, the performance was similar, further supporting the importance of considering the cogwheel rigidity to better infer about the reduction in rigidity. Overall, we present a simple, wearable, mobile system, suitable for intra-operatory conditions during DBS, supporting a physician in decision-making when setting stimulation parameters.
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