Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction

Cheng, Yujiao; Sun, Liting; Liu, Changliu; Tomizuka, Masayoshi

doi:10.1109/lra.2020.2972874

Cited by 78 publications

(50 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key of this approach is to predict the human motion for tasks not known a priori by learning an unknown cost function that describes the human movements. An integrated framework that includes plan recognition and trajectory prediction modules is proposed by Cheng et al in [67]. More specifically, a robust plan recognition algorithm based on neural networks and Bayesian inference is implemented by the authors.…”

Section: Motion Planningmentioning

confidence: 99%

“…In [58] and [55], the productivity is maintained at the maximum level, while guaranteeing safety. In [67], Cheng et al aim not only at improving safety but also at minimizing the task completion time. Lastly, in [57] high profitability, efficiency, and reduced cycle time are obtained by minimizing unnecessary robot stops and slowdowns in case of false-positive human detection.…”

Section: A Motion Planningmentioning

confidence: 99%

“…As a consequence, there is an emerging need of tackling uncertainties while keeping the controller's performance and robustness at the highest level. To cope with this issue, hybrid control techniques that combine NNs with admittance/impedance control [22], [23], stochastic control techniques [24], [67], and optimization-based methods [56], [68] are introduced in some of the selected papers and could be the turning point in future research in collaborative robotics.…”

Section: B Emerging Control Issues and Challengesmentioning

confidence: 99%

See 2 more Smart Citations

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Proia

Carli

Cavone

et al. 2022

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

The fourth industrial revolution, also known as Industry 4.0, is reshaping the way individuals live and work while providing a substantial influence on the manufacturing scenario.The key enabling technology that has made Industry 4.0 a concrete reality is without doubt collaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of employees' safety and well-being, together with the increase of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this paper presents an accurate review of the most recent and relevant contributions to the related literature, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and the promising future developments in the context of the digital evolution.Note to Practitioners-The design and development of manufacturing systems are experiencing substantial changes towards full automation. This ongoing challenge is being tackled by academia and industrial practitioners with the adoption of collaborative robots, where the skills and peculiarities of humans (e.g., intelligence, creativity, adaptability, etc.) and robots (e.g., flexibility, pinpoint accuracy, tirelessness, etc.) are combined to better perform a variety of tasks. Nevertheless, due to their different characteristics, there is an emerging need for designing suitable decision and control techniques to ensure a safe and ergonomic HRC, while keeping the highest level of productivity. Against this background, the aim of this paper is to provide researchers and practitioners with a reference source in the related field, which can help them designing and developing suitable solutions to control problems in safe, ergonomic, and efficient collaborative robotics.

show abstract

Section: Motion Planningmentioning

confidence: 99%

Section: A Motion Planningmentioning

confidence: 99%

Section: B Emerging Control Issues and Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Proia

Carli

Cavone

et al. 2022

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Liu et al presented a probabilistic model for human motion prediction for task-level human-robot collaborative assembly [28]. Cheng et al proposed an integrated framework for human-robot collaboration in which the robot perceives and adapts to human actions [29].…”

Section: Motion/task Planning Based On Human Motion Predictionmentioning

confidence: 99%

Human-Following Motion Planning and Control Scheme for Collaborative Robots Based on Human Motion Prediction

Iqbal¹,

Kanazawa²,

Kinugawa³

et al. 2021

Preprint

View full text Add to dashboard Cite

Human-Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist the human workers in their tasks and improve their efficiency. But the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC) based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker.

show abstract

“…Augmented Reality (AR) could be used, reducing the number of engineering/production management resources needed to provide assembly operators with cognitive support to perform their tasks [45,46]; as well as cognitive/handling skills transfer systems [47], self-adapting automatic quality control [48] or cognitive automation strategies [49]. Automation needs to ensure human safety, which led to research on Human-Robot Collaboration (HRC) plan recognition and trajectory prediction [50], and the concept of "safety bubble" [51]. When employing novel digital technologies for enhancing assembly systems performance, one cannot underestimate the strategic importance of IT/IS systems [52].…”

Section: Mass Customisation Impacts Operatorsmentioning

confidence: 99%

Lean Manual Assembly 4.0: A Systematic Review

2020

View full text Add to dashboard Cite

In a demand context of mass customization, shifting towards the mass personalization of products, assembly operations face the trade-off between highly productive automated systems and flexible manual operators. Novel digital technologies—conceptualized as Industry 4.0—suggest the possibility of simultaneously achieving superior productivity and flexibility. This article aims to address how Industry 4.0 technologies could improve the productivity, flexibility and quality of assembly operations. A systematic literature review was carried out, including 234 peer-reviewed articles from 2010–2020. As a result, the analysis was structured addressing four sets of research questions regarding (1) assembly for mass customization; (2) Industry 4.0 and performance evaluation; (3) Lean production as a starting point for smart factories, and (4) the implications of Industry 4.0 for people in assembly operations. It was found that mass customization brings great complexity that needs to be addressed at different levels from a holistic point of view; that Industry 4.0 offers powerful tools to achieve superior productivity and flexibility in assembly; that Lean is a great starting point for implementing such changes; and that people need to be considered central to Assembly 4.0. Developing methodologies for implementing Industry 4.0 to achieve specific business goals remains an open research topic.

show abstract

Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction

Cited by 78 publications

References 18 publications

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Human-Following Motion Planning and Control Scheme for Collaborative Robots Based on Human Motion Prediction

Lean Manual Assembly 4.0: A Systematic Review

Contact Info

Product

Resources

About