Article
Computer Science, Artificial Intelligence
Weidong Li, Yudie Hu, Yong Zhou, Duc Truong Pham
Summary: In this survey paper, the importance of human-robot collaboration (HRC) in the industry is highlighted. The paper provides an update on standards and implementation approaches for HRC safety, covering industrial robots, collaborative robots, and HRC. Various approaches to HRC safety are surveyed from the perspectives of pre-collision and post-collision, including sensing, prediction, learning, planning/replanning, and compliance control. The paper also discusses challenging issues and future prospects for HRC safety, providing recommendations for stakeholders in designing HRC-enabled industrial systems.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Automation & Control Systems
Gaoyang Pang, Geng Yang, Wenzheng Heng, Zhiqiu Ye, Xiaoyan Huang, Hua-Yong Yang, Zhibo Pang
Summary: The new collaborative robot skin (CoboSkin) enhances safety in human-robot collaboration by providing softness, variable stiffness, and sensitivity features. It is composed of inflatable and sensing units, with the ability to measure distributed contact force and adjust stiffness by varying air pressure.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Chemistry, Analytical
Pu Zheng, Pierre-Brice Wieber, Junaid Baber, Olivier Aycard
Summary: Industry 4.0 transforms classical industrial systems into more human-centric and digitized systems. Close human-robot collaboration is becoming more frequent, making security and efficiency issues crucial. This paper proposes equipping robots with exteroceptive sensors and online motion generation, allowing them to perceive and predict human trajectories and react accordingly to avoid collisions. The proposed framework is validated in a real environment, ensuring collision-free collaboration between humans and robots in a shared workspace.
Article
Computer Science, Interdisciplinary Applications
Kelly Merckaert, Bryan Convens, Chi-ju Wu, Alessandro Roncone, Marco M. Nicotra, Bram Vanderborght
Summary: This paper introduces a computationally efficient control scheme for safe human-robot interaction, relying on the Explicit Reference Governor (ERG) formalism to enforce input and state constraints in real-time. The proposed solution has been theoretically supported and experimentally validated on the Franka Emika Panda robotic manipulator.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Interdisciplinary Applications
Yong Zhou, Yiqun Peng, Weidong Li, Duc Truong Pham
Summary: This paper proposes an innovative human-robot collaboration (HRC) approach enabled by the Stackelberg model to remove screws in end-of-life (EoL) products. The approach takes into account the safety and disassembly efficiency of both human operators and robots, and achieves the best performance through a Particle Swarm Optimization (PSO)-Pareto algorithm.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Interdisciplinary Applications
Maram Khatib, Khaled Al Khudir, Alessandro De Luca
Summary: A control system based on multiple sensors is proposed for safe collaboration between a robot and a human. New motion tasks aim to control the robot end-effector to maintain a desired relative position to the human head, while avoiding collisions with the operator and other obstacles. The system integrates direct human-robot communication through a mixed reality interface for collaborative quality assessment phases in a manufacturing process.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Interdisciplinary Applications
Yanzhe Wang, Lai Wei, Kunpeng Du, Gongping Liu, Qian Yang, Yanding Wei, Qiang Fang
Summary: The paper proposes a new online collision avoidance trajectory planning algorithm for ensuring human safety during collaborative tasks. The algorithm consists of trajectory generation and local optimization. A neural network trajectory planner called CWP-net is introduced, which generates key waypoints for dynamic obstacle avoidance based on DPGMM distribution learning. The generated trajectories are then locally optimized using an improved STOMP algorithm, constraining the optimization range and direction through the DPGMM model. Simulations and real experiments demonstrate the algorithm's ability to smoothly adjust paths and effectively avoid collisions in human-robot collaboration scenarios.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Computer Science, Artificial Intelligence
Quan Liu, Zhihao Liu, Bo Xiong, Wenjun Xu, Yang Liu
Summary: This paper introduces a deep reinforcement learning approach for real-time collision-free motion planning of an industrial robot, aiming to ensure operator safety in human-robot collaboration in manufacturing. By optimizing the reward function and combining the DDPG algorithm, the proposed IRDDPG algorithm allows the robot to learn an expected collision avoidance policy effectively in a simulation environment.
ADVANCED ENGINEERING INFORMATICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Achim Buerkle, Thomas Bamber, Niels Lohse, Pedro Ferreira
Summary: Manufacturing challenges are driving collaboration between humans and robots in the same workspace. Safety is a critical factor, where human operators play a key role despite advances in safety systems. A novel approach using human sensors and EEG data shows promising potential for enhancing safety in Human-Robot Collaboration.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Automation & Control Systems
Silvia Proia, Raffaele Carli, Graziana Cavone, Mariagrazia Dotoli
Summary: The fourth industrial revolution, also known as Industry 4.0, is reshaping the way individuals live and work, with a substantial impact on the manufacturing sector. Collaborative robotics is the key enabling technology behind Industry 4.0 and is evolving as a fundamental pillar of Industry 5.0. The main goals of human-robot collaboration in the industrial setting are to improve employee safety and well-being, while increasing profitability and productivity.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Review
Chemistry, Analytical
Andrea Bonci, Pangcheng David Cen Cheng, Marina Indri, Giacomo Nabissi, Fiorella Sibona
Summary: The capability of perception is crucial for human-robot interaction in industrial environments. As industrial settings require higher levels of automation to meet fast-paced and cost-effective market demands, robots must be able to sense human presence and intentions to prevent decreases in productivity and safety hazards. Robots with enhanced perception and interaction capabilities will play an increasingly important role in collaborative and cooperative applications within shared industrial spaces.
Article
Robotics
Andrea Pupa, Mohammad Arrfou, Gildo Andreoni, Cristian Secchi
Summary: The new paradigm of human-robot collaboration has led to the creation of shared work environments, which has prompted an update in safety regulations to address the safety challenges that come with close interaction between humans and robots. In order to ensure efficiency and safety in robot operations, a two layers architecture for trajectory planning and scaling has been proposed.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Md Khurram Monir Rabby, Ali Karimoddini, Mubbashar Altaf Khan, Steven Jiang
Summary: This article proposes an adjustable autonomy framework for robot operation in human-robot collaboration scenarios. Through reinforcement learning and an integrated epsilon-greedy approach, the robot can autonomously adjust its actions based on rewards from a human operator. Experimental results confirm the effectiveness of the framework in various situations.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Industrial
Antonio Giallanza, Giada La Scalia, Rosa Micale, Concetta Manuela La Fata
Summary: Human-Robot Collaboration (HRC) is vital for advancing sustainable production systems. This study provides a comprehensive overview of Occupational Health and Safety (OHS) issues in HRC through bibliometric analysis and literature review. The research identifies the most studied topics and application areas and suggests future research directions. The importance of considering the human factor and implementing human-centered design and cognitive engineering principles to increase worker acceptance and trust in collaboration is emphasized.
Article
Automation & Control Systems
Wansong Liu, Xiao Liang, Minghui Zheng
Summary: This article presents a new method for generating task-constrained and collision-free motion for a collaborative robot operating in a dynamic environment involving human movement. The method takes into consideration the high degree of freedom of the corobot and the uncertainty nature of human motion to ensure efficient and safe collaboration.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Automation & Control Systems
S. M. Mizanoor Rahman, Yue Wang
Review
Engineering, Civil
Ankur Sarker, Haiying Shen, Mizanur Rahman, Mashrur Chowdhury, Kakan Dey, Fangjian Li, Yue Wang, Husnu S. Narman
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Robotics
Maziar Fooladi Mahani, Longsheng Jiang, Yue Wang
Summary: A Bayesian inference model is developed for the degree of human trust in multiple mobile robots, incorporating a linear model for robot performance and a computational trust model based on a dynamic Bayesian network. Through the use of categorical Boltzmann machine and expectation maximization algorithm, the model can infer human trust in robots and predict human interactions with high accuracy. This study confirms the effectiveness of DBNs in modeling human trust towards multi-robot systems.
INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
(2021)
Article
Automation & Control Systems
Huanfei Zheng, Yue Wang
Summary: This paper presents an automaton-based task and motion planning framework for multi-robot systems to satisfy task specifications in parallel. The framework decomposes global task specifications into smaller subtask automata and assigns robots accordingly. Experimental results demonstrate the framework's scalability and execution efficiency.
DISCRETE EVENT DYNAMIC SYSTEMS-THEORY AND APPLICATIONS
(2022)
Article
Engineering, Civil
Fangjian Li, Chengshi Wang, Dariusz Mikulski, John R. Wagner, Yue Wang
Summary: In this paper, a human-robot interaction (HRI) framework is proposed to enhance the resilience of unmanned ground vehicle (UGV) platoons under cyber attacks. The framework includes an observer-based autonomous resilient control strategy and a decision-making aid system for human supervision. Simulation and experiments show that the proposed framework can effectively guide human operators, reducing platoon vulnerability and human workload.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Longsheng Jiang, Yue Wang
Summary: A computational model is proposed to enable robots to make decisions under risk in a human-like way, incorporating psychological effects such as regret theory. The model is further quantified, trained with individual preference data, and shown to have high prediction accuracy compared to human decision-making.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Longsheng Jiang, Yue Wang
Summary: This study examines the effects of risk-awareness in a human-multi-robot collaborative search task and proposes using an extended version of regret theory (RTx) for risk-aware decision-making. The experimental results in simulation demonstrate the advantages of risk-aware decision-making over expected value decision-making.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2022)
Article
Engineering, Civil
Longsheng Jiang, Dong Chen, Zhaojian Li, Yue Wang
Summary: This study proposes a computational model that integrates perception, reasoning, emotion, and decision-making to describe driver lane-change decision-making. The model performs well in modeling risk perception and risk propensity, and shows better prediction performance than other models.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Robotics
Huanfei Zheng, Jonathon M. M. Smereka, Dariusz Mikulski, Yue Wang
Summary: In this paper, a computational trust model for a human to multi-robot system (MRS) is developed to encode human intention into MRS motion tasks in offroad environments. Bayesian inference is used to derive the posterior distribution of trust model parameters, and a Markov Chain Monte Carlo sampling algorithm is developed to approximate the distributions. A Bayesian optimization based experimental design is proposed to learn the human-MRS trust model parameters sequentially, and a case study is conducted to validate the effectiveness of the trust model.
INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
(2023)
Article
Engineering, Civil
Dong Chen, Mohammad R. Hajidavalloo, Zhaojian Li, Kaian Chen, Yongqiang Wang, Longsheng Jiang, Yue Wang
Summary: In this paper, the on-ramp merging problem in mixed traffic is formulated as a multi-agent reinforcement learning (MARL) problem, where autonomous vehicles (AVs) collaboratively learn to adapt to human-driven vehicles (HDVs) and maximize traffic throughput. An efficient and scalable MARL framework is developed to handle dynamic traffic with time-varying communication topology. The framework utilizes parameter sharing, local rewards, action masking, and a priority-based safety supervisor to encourage inter-agent cooperation, improve learning efficiency, and reduce collision rates. Experimental results show that the proposed MARL framework consistently outperforms several state-of-the-art benchmarks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Proceedings Paper
Automation & Control Systems
Jared Chamberlin, Yehua Zhong, Yue Wang
Summary: This paper presents an automated robotic system for manufacturing polymer syringes, demonstrating the automated filling and stoppering processes, as well as the concept of a complete manufacturing system.
Article
Automation & Control Systems
Chong Tian, Shahil Shaik, Yue Wang
Summary: The study developed a deep learning-based shared control framework that optimizes the assistance of human operators in teleoperating mobile robots, reducing collision numbers and workload.
IET CYBER-SYSTEMS AND ROBOTICS
(2021)
Article
Robotics
Hamed Saeidi, Yue Wang
IEEE ROBOTICS AND AUTOMATION LETTERS
(2019)
Article
Computer Science, Artificial Intelligence
Yue Wang, Laura R. Humphrey, Zhanrui Liao, Huanfei Zheng
ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS
(2018)