摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的研究发现被用在一份新的报告中。根据NewsRx记者从西班牙马德里发回的新闻报道,研究表明:“在未知环境中探索任务已经成为一种相关的搜索和救援机器人方法。地面机器人是第一次探索时救援人员的更好选择。”这项研究的资助者包括马德里社区I+D活动计划、欧盟结构基金、科技部I+D+I项目、创新和大学项目、合作搜索和救援Robo TS(CESAR)、ERDF成为欧洲的一种方式。新闻记者从马德里理工大学的研究中获得了一句话:“然而,探索的进展往往受到不均匀的地形的限制,这些地形超过了机器人的运动能力,”本文提出了一种基于协作行为克服均匀地形的创新解决方案，实现了一种采用两个协作机器人的方法，这些机器人设计成有袋状结构，以克服不均匀地形。这两个机器人分别表示为1(增强了移动坡道)和2(充当探险者)，在(RGB-D)感知系统的基础上,实现了一个状态机来管理任务的预测,实现了任务的决策和自主执行。在初始阶段,利用点云和无监督学习对任务的搜索区域和上升区域进行了特征描述。第二阶段利用人工视觉算法和信标控制2号坡道的爬坡来管理机器人之间的交互，并进行了室外试验验证了该方法的有效性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are disc ussed in a new report. According to news reporting originating in Madrid, Spain, by NewsRx journalists, research stated, "Exploring tasks in unknown environment s has become a relevant search and rescue robotics approach. Ground robots are a better alternative to rescuers for first exploration." Funders for this research include Programas de Actividades I + D en la Comunidad Madrid, Structural Funds of the EU, Proyectos de I + D + i del Ministerio de Ci encia, Innovacion y Universidades, Proyecto CollaborativE Search And Rescue robo ts (CESAR), ERDF A way of making Europe. The news reporters obtained a quote from the research from the Polytechnic Unive rsity of Madrid, "However, exploration progress is often limited by uneven terra ins that exceed the kinematic capabilities of robots, including those with compl ex locomotion systems. This work proposes an innovative solution based on collab orative behaviours to overcome even terrains. A method employing two collaborati ve robots designed to operate in a marsupial configuration to surmount uneven te rrains has been implemented. These robots, denoted as R1 (enhanced with a mobile ramp) and R2 (serving as an explorer), interact synergistically to expand the e xplored area autonomously. A state machine has been implemented to manage the pr ogression of the mission, based on a perception (RGB-D) system, for both decisio n - making and autonomous execution of the process. In the initial stage, the ter rain and ascent zones to be explored are characterized using point clouds and un supervised learning. Subsequently, the second stage manages the interaction betw een the robots by controlling the R2 ascent through the R1 ramp using artificial vision algorithms and beacons. Outdoor tests have been performed to validate th e method."
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