Pub Date : 2024-01-15DOI: 10.1109/mra.2023.3336472
Murilo Marques Marinho, Juan José Quiroz-Omaña, Kanako Harada
{"title":"A Multiarm Robotic Platform for Scientific Exploration: Its Design, Digital Twins, and Validation","authors":"Murilo Marques Marinho, Juan José Quiroz-Omaña, Kanako Harada","doi":"10.1109/mra.2023.3336472","DOIUrl":"https://doi.org/10.1109/mra.2023.3336472","url":null,"abstract":"","PeriodicalId":55019,"journal":{"name":"IEEE Robotics & Automation Magazine","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-10DOI: 10.1109/mra.2023.3348303
Liang Li, Li-Ming Chao, Siyuan Wang, Oliver Deussen, Iain D. Couzin
{"title":"RoboTwin: A Platform to Study Hydrodynamic Interactions in Schooling Fish","authors":"Liang Li, Li-Ming Chao, Siyuan Wang, Oliver Deussen, Iain D. Couzin","doi":"10.1109/mra.2023.3348303","DOIUrl":"https://doi.org/10.1109/mra.2023.3348303","url":null,"abstract":"","PeriodicalId":55019,"journal":{"name":"IEEE Robotics & Automation Magazine","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1109/mra.2023.3336473
Peter So, Andriy Sarabakha, Fan Wu, Utku Culha, Fares J. Abu-Dakka, Sami Haddadin
{"title":"Digital Robot Judge: Building a Task-centric Performance Database of Real-World Manipulation With Electronic Task Boards","authors":"Peter So, Andriy Sarabakha, Fan Wu, Utku Culha, Fares J. Abu-Dakka, Sami Haddadin","doi":"10.1109/mra.2023.3336473","DOIUrl":"https://doi.org/10.1109/mra.2023.3336473","url":null,"abstract":"","PeriodicalId":55019,"journal":{"name":"IEEE Robotics & Automation Magazine","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An underwater hexapod robot, driven by six C-shaped legs and eight thrusters, has the potential to traverse diverse terrains with unknown deformable properties, which can lead to unknown leg–terrain interaction forces. However, it is hard to use exteroceptive sensors such as cameras and sonars to recognize these properties. Here we propose a method to perceive the interaction forces and feed them into a controller for determining thrust inputs. The key idea lies in using supervised learning to obtain the properties from reliable proprioceptive sensory data. First, we propose a new expression called zero moment point (ZMP) bias that can indirectly represent the leg–terrain interaction force, removing the effects caused by gravity, buoyancy, and thrust. Second, we gather a walking cycle’s discrete ZMP biases and then parameterize them as polynomials. Third, we use several previous walking cycles’ parameterized biases to predict the current walking cycle’s biases to generate the needed pitch and roll moments. Finally, we propose a terrain-adaptive locomotion controller for the robot, which incorporates these moments into a base control module and uses thrust to compensate for the interaction force for smooth walking. Extensive indoor pool and wild lake hardware experiments confirm our method’s effectiveness.
由六条 C 形腿和八个推进器驱动的水下六足机器人有可能穿越具有未知变形特性的各种地形，这可能导致未知的腿-地形相互作用力。然而，很难使用外部感知传感器（如摄像头和声纳）来识别这些属性。在这里，我们提出了一种感知相互作用力并将其输入控制器以确定推力输入的方法。其关键在于利用监督学习从可靠的本体感觉数据中获取属性。首先，我们提出了一种名为零力矩点（ZMP）偏差的新表达式，它可以间接表示腿部与地形的相互作用力，并消除重力、浮力和推力造成的影响。其次，我们收集一个行走周期的离散 ZMP 偏置，然后将其参数化为多项式。第三，我们利用之前几个行走周期的参数化偏置来预测当前行走周期的偏置，从而产生所需的俯仰力矩和滚动力矩。最后，我们为机器人提出了一种地形适应性运动控制器，它将这些力矩纳入基本控制模块，并利用推力补偿相互作用力，从而实现平稳行走。大量的室内泳池和野外湖泊硬件实验证实了我们方法的有效性。
{"title":"Terrain-Adaptive Locomotion Control for an Underwater Hexapod Robot: Sensing Leg–Terrain Interaction With Proprioceptive Sensors","authors":"Lepeng Chen, Rongxin Cui, Weisheng Yan, Hui Xu, Shouxu Zhang, Haitao Yu","doi":"10.1109/mra.2023.3341247","DOIUrl":"https://doi.org/10.1109/mra.2023.3341247","url":null,"abstract":"An underwater hexapod robot, driven by six C-shaped legs and eight thrusters, has the potential to traverse diverse terrains with unknown deformable properties, which can lead to unknown leg–terrain interaction forces. However, it is hard to use exteroceptive sensors such as cameras and sonars to recognize these properties. Here we propose a method to perceive the interaction forces and feed them into a controller for determining thrust inputs. The key idea lies in using supervised learning to obtain the properties from reliable proprioceptive sensory data. First, we propose a new expression called zero moment point (ZMP) bias that can indirectly represent the leg–terrain interaction force, removing the effects caused by gravity, buoyancy, and thrust. Second, we gather a walking cycle’s discrete ZMP biases and then parameterize them as polynomials. Third, we use several previous walking cycles’ parameterized biases to predict the current walking cycle’s biases to generate the needed pitch and roll moments. Finally, we propose a terrain-adaptive locomotion controller for the robot, which incorporates these moments into a base control module and uses thrust to compensate for the interaction force for smooth walking. Extensive indoor pool and wild lake hardware experiments confirm our method’s effectiveness.","PeriodicalId":55019,"journal":{"name":"IEEE Robotics & Automation Magazine","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1109/mra.2023.3322920
Xuesu Xiao, Zifan Xu, Garrett Warnell, Peter Stone, Ferran Gebelli Guinjoan, R么mulo T. Rodrigues, Herman Bruyninckx, Hanjaya Mandala, Guilherme Christmann, Jose Luis Blanco-Claraco, Shravan Somashekara Rai
The second Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023) in London, U.K., and continued to evaluate the performance of state-of-the-art autonomous ground navigation systems in highly constrained environments. Compared to the first BARN Challenge at ICRA 2022 in Philadelphia, the competition has grown significantly in size, doubling the numbers of participants in both the simulation qualifier and physical finals: 10 teams from all over the world participated in the qualifying simulation competition, six of which were invited to compete with each other in three physical obstacle courses at the conference center in London. Three teams won the challenge by navigating a Clearpath Jackal robot from a predefined start to a goal with the shortest amount of time without colliding with any obstacle. The competition results, compared to those of last year, suggest that the teams are making progress toward more robust and efficient ground navigation systems that work out of the box in many obstacle environments. However, a significant amount of fine-tuning is still needed on site to cater to different difficult navigation scenarios. Furthermore, challenges still remain for many teams when facing extremely cluttered obstacles and increasing navigation speed. In this article, we discuss the challenge, the approaches used by the three winning teams, and lessons learned to direct future research.
第二届基准自主机器人导航（BARN）挑战赛在英国伦敦举行的 2023 年电气和电子工程师学会机器人与自动化国际会议（ICRA 2023）上举行，继续评估最先进的自主地面导航系统在高度受限环境中的性能。与 2022 年费城 ICRA 上的首届 BARN 挑战赛相比，本次比赛的规模显著扩大，参加模拟预选赛和物理决赛的人数翻了一番：来自世界各地的 10 支队伍参加了模拟预选赛，其中六支队伍受邀在伦敦会议中心的三个物理障碍赛道上一决高下。三支参赛队通过驾驶 Clearpath Jackal 机器人在最短时间内从预定起点到达目标，且未与任何障碍物发生碰撞，赢得了挑战赛。与去年的比赛结果相比，今年的比赛结果表明，参赛团队在开发更强大、更高效的地面导航系统方面正在取得进展，这些系统在许多障碍物环境中都能正常工作。不过，现场仍需要进行大量的微调，以适应不同的导航困难情况。此外，许多团队在面对极其杂乱的障碍物和提高导航速度时仍然面临挑战。在本文中，我们将讨论此次挑战赛、三支获胜队伍所采用的方法，以及指导未来研究的经验教训。
{"title":"Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Second BARN Challenge at ICRA 2023 [Competitions]","authors":"Xuesu Xiao, Zifan Xu, Garrett Warnell, Peter Stone, Ferran Gebelli Guinjoan, R么mulo T. Rodrigues, Herman Bruyninckx, Hanjaya Mandala, Guilherme Christmann, Jose Luis Blanco-Claraco, Shravan Somashekara Rai","doi":"10.1109/mra.2023.3322920","DOIUrl":"https://doi.org/10.1109/mra.2023.3322920","url":null,"abstract":"The second Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023) in London, U.K., and continued to evaluate the performance of state-of-the-art autonomous ground navigation systems in highly constrained environments. Compared to the first BARN Challenge at ICRA 2022 in Philadelphia, the competition has grown significantly in size, doubling the numbers of participants in both the simulation qualifier and physical finals: 10 teams from all over the world participated in the qualifying simulation competition, six of which were invited to compete with each other in three physical obstacle courses at the conference center in London. Three teams won the challenge by navigating a Clearpath Jackal robot from a predefined start to a goal with the shortest amount of time without colliding with any obstacle. The competition results, compared to those of last year, suggest that the teams are making progress toward more robust and efficient ground navigation systems that work out of the box in many obstacle environments. However, a significant amount of fine-tuning is still needed on site to cater to different difficult navigation scenarios. Furthermore, challenges still remain for many teams when facing extremely cluttered obstacles and increasing navigation speed. In this article, we discuss the challenge, the approaches used by the three winning teams, and lessons learned to direct future research.","PeriodicalId":55019,"journal":{"name":"IEEE Robotics & Automation Magazine","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138685282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1109/mra.2023.3323104
Francesco Missiroli
Wine, often referred to as the nectar of the gods , has a rich history dating back thousands of years. It’s a product of nature, human skill, and tradition, encapsulated in a glass. People have often regarded this beverage as a true gift from the gods; from the Roman Bacchus to the Greek Dionysus to the Egyptian god Osiris, countless are the deities with whom the origin of wine is associated. The stories and myths related to it are varied and numerous, enchanting and incredibly seductive legends. Wine has been a companion to humanity from its earliest days, holding significant roles in various sociological, cultural, and religious aspects. Today, it maintains its paramount position globally, being of profound importance in terms of both consumption and production.
{"title":"The Art of Wine and Its Harvesting Methods [Student’s Corner]","authors":"Francesco Missiroli","doi":"10.1109/mra.2023.3323104","DOIUrl":"https://doi.org/10.1109/mra.2023.3323104","url":null,"abstract":"Wine, often referred to as the <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">nectar of the gods</i>\u0000, has a rich history dating back thousands of years. It’s a product of nature, human skill, and tradition, encapsulated in a glass. People have often regarded this beverage as a true gift from the gods; from the Roman Bacchus to the Greek Dionysus to the Egyptian god Osiris, countless are the deities with whom the origin of wine is associated. The stories and myths related to it are varied and numerous, enchanting and incredibly seductive legends. Wine has been a companion to humanity from its earliest days, holding significant roles in various sociological, cultural, and religious aspects. Today, it maintains its paramount position globally, being of profound importance in terms of both consumption and production.","PeriodicalId":55019,"journal":{"name":"IEEE Robotics & Automation Magazine","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138685284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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