Article
Robotics
Zhongyu Li, Jun Zeng, Shuxiao Chen, Koushil Sreenath
Summary: This paper presents an end-to-end autonomous navigation framework for bipedal robots to safely explore height-constrained environments. It leverages three layers of planners and a variable walking height controller to optimize trajectory plans and maintain stable periodic walking gaits. Experimental results with a bipedal robot Cassie demonstrate the reliability of the framework in avoiding obstacles and reaching the goal location in various cluttered environments.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2023)
Review
Chemistry, Analytical
Lefteris Benos, Vasileios Moysiadis, Dimitrios Kateris, Aristotelis C. Tagarakis, Patrizia Busato, Simon Pearson, Dionysis Bochtis
Summary: In order to improve the efficiency, flexibility, and adaptability of agricultural practices, human-robot interaction (HRI) has been introduced in agriculture with the help of advancing information and communication technologies. This paper provides a systematic review of scholarly literature in this field, highlighting the progress, trends, and future research directions. The findings show a growing interest in HRI applications, combining perspectives from various disciplines for a comprehensive understanding. The selected papers focused on synergistic target detection with simulation as the main methodology, and melons, grapes, and strawberries emerged as the crops with the highest interest. Collaboration and cooperation were found to be the preferred interaction modes, with different levels of automation examined. The synergy of humans and robots demonstrated the best results in terms of system performance, worker workload, and task execution time, but establishing viable, functional, and safe human-robot interactive systems still requires further progress.
Article
Robotics
Han Hu, Kaicheng Zhang, Aaron Hao Tan, Michael Ruan, Christopher Agia, Goldie Nejat
Summary: The study presented a novel sim-to-real pipeline for a mobile robot to effectively learn how to navigate real-world 3D rough terrain environments, with experiments showing that our method outperformed classical and deep learning-based approaches in terms of success rate, cumulative travel distance, and time in a 3D rough terrain environment.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Max Schmidt, Jerome Kirchhoff, Oskar von Stryk
Summary: Autonomous service robots have great potential in supporting humans in tasks they cannot perform. Transparency is crucial for these robots to gain trust and acceptance from society. A black box recorder can enhance transparency by facilitating incident investigation, clarifying responsibilities, and improving user understanding. This work proposes the requirements and presents a modular and portable design of a black box recorder for autonomous service robots.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Nuria Pena Perez, Jonathan Eden, Ekaterina Ivanova, Etienne Burdet, Ildar Farkhatdinov
Summary: By perturbing the haptic and visual feedback of the right hand, the effort distribution between the two hands can be altered. Visual disturbance was more effective in causing participants to rely solely on their unperturbed hand.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Gregory Kahn, Pieter Abbeel, Sergey Levine
Summary: Traditional mobile robot navigation solutions focus on the geometric structure of the environment, but this approach may not always be effective. BADGR utilizes a reinforcement learning approach to move beyond purely geometric navigation solutions, learning physical navigational affordances in order to navigate mobile robots without the need for simulation or human supervision.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Sahba Aghajani Pedram, Changyeob Shin, Peter Walker Ferguson, Ji Ma, Erik P. Dutson, Jacob Rosen
Summary: This study presents an autonomous suturing framework that utilizes a novel needle path planner, accurate needle pose estimator, and six degrees-of-freedom controller to achieve high accuracy in needle pose estimation across all directions. The proposed framework drastically improves suture performance compared to traditional methods, successfully delivering desired clinical parameters across tissue phantom environments with different mechanical properties and needle trajectories.
IEEE TRANSACTIONS ON ROBOTICS
(2021)
Article
Robotics
Jialiang Hou, Xin Zhou, Zhongxue Gan, Fei Gao
Summary: Designing autonomous aerial robot team systems is a significant challenge in robotics. This paper proposes an enhanced decentralized system with group planning, improving planning quality through efficient multi-agent pathfinding and trajectory optimization.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Cheng Fang, Luka Peternel, Ajay Seth, Massimo Sartori, Katja Mombaur, Eiichi Yoshida
Summary: The advancement of human modeling and robotics have common interests and interconnections. The principles used in robotics, such as multibody dynamics and control theory, have greatly benefited human modeling. With the development of safer and smarter robots, they actively participate in our lives and assist us. By combining human modeling and robotic methods in physical human-robot interaction, it can lead to improved human understanding and functional assistance.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Nicolo Pasini, Andrea Mariani, Anton Deguet, Peter Kazanzides, Elena De Momi
Summary: This article introduces an online surgical Gesture Recognition for Autonomous Camera-motion Enhancement (GRACE) system, which aims to introduce situation awareness in camera navigation during robotic-assisted surgery. Compared to current autonomous systems and clinical approaches, GRACE improves completion time and reduces workload.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Mario Selvaggio, Marco Cognetti, Stefanos Nikolaidis, Serena Ivaldi, Bruno Siciliano
Summary: Sharing control of a robotic system with an autonomous controller allows humans to reduce cognitive and physical workload during tasks, and recent developments in inference and learning techniques have expanded the applications of shared control approaches. This enables robotic systems to seamlessly adapt their autonomy levels. This letter compiles the latest research results in shared control and shared autonomy, with a focus on physical human-robot interaction, discussing architectures and methods developed for shared control and shared autonomy. The letter concludes with a discussion on open issues.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Chemistry, Analytical
Andrea Pupa, Wietse Van Dijk, Christiaan Brekelmans, Cristian Secchi
Summary: This paper proposes an online framework to address task scheduling challenges in collaborative robotics, specifically in an industrial setting, by handling uncertainties and promoting parallel human-robot work.
Article
Computer Science, Cybernetics
Milan Zorman, Bojan Zlahtic, Sasa Stradovnik, Ales Hace
Summary: This article discusses the current state and future trends of collaborative robotics and autonomous driving, and proposes the transfer of meta-knowledge to accelerate progress. The researchers believe that in the coming years, autonomous driving and collaborative robotics will converge and merge in certain areas.
Article
Automation & Control Systems
Zongyao Jin, Prabhakar R. Pagilla
Summary: In this article, a novel method for subgoal identification from a robotics task demonstrated by a human operator is described. A unified metric is defined to quantify the human operator's commands, which helps in extracting subgoal distributions and identifying subgoals effectively. The article also addresses the problem of online subgoal adjustment for shared control applications and provides a practical implementation and experimental results.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Robotics
Costanza Messeri, Gabriele Masotti, Andrea Maria Zanchettin, Paolo Rocco
Summary: In human-robot collaboration, maximizing productivity while reducing operator stress is crucial. A novel paradigm is proposed to adapt robot behavior in real-time to optimize human physiological stress and productivity. The study demonstrates that this control strategy effectively enhances the productivity of the human-robot team while mitigating operator stress.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)