scholarly journals A Practical and Effective Layout for a Safe Human-Robot Collaborative Assembly Task

2021 ◽  
Vol 11 (4) ◽  
pp. 1763
Author(s):  
Leonardo Sabatino Scimmi ◽  
Matteo Melchiorre ◽  
Mario Troise ◽  
Stefano Mauro ◽  
Stefano Pastorelli
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Microsoft Kinect ◽  
Single Camera ◽  
Significant Gain ◽  
Assembly Task ◽  
Collaborative Assembly ◽  
Pick And Place ◽  
Human Robot Collaboration ◽  
Collaborative Robot

This work describes a layout to carry out a demonstrative assembly task, during which a collaborative robot performs pick-and-place tasks to supply an operator the parts that he/she has to assemble. In this scenario, the robot and operator share the workspace and a real time collision avoidance algorithm is implemented to modify the planned trajectories of the robot avoiding any collision with the human worker. The movements of the operator are tracked by two Microsoft Kinect v2 sensors to overcome problems related with occlusions and poor perception of a single camera. The data obtained by the two Kinect sensors are combined and then given as input to the collision avoidance algorithm. The experimental results show the effectiveness of the collision avoidance algorithm and the significant gain in terms of task times that the highest level of human-robot collaboration can bring.

Task allocation for improved ergonomics in Human-Robot Collaborative Assembly

2019 ◽  
Vol 20 (1) ◽  
pp. 102-133 ◽  
Author(s):  
Ilias El Makrini ◽  
Kelly Merckaert ◽  
Joris De Winter ◽  
Dirk Lefeber ◽  
Bram Vanderborght
Keyword(s):  
Task Allocation ◽  
Health Concern ◽  
Production Environment ◽  
Task Requirements ◽  
Collaborative Assembly ◽  
Agent Characteristics ◽  
Human Robot Collaboration ◽  
Collaborative Robot ◽  
And Task

Abstract Human-robot collaboration, whereby the human and the robot join their forces to achieve a task, opens new application opportunities in manufacturing. Robots can perform precise and repetitive operations while humans can execute tasks that require dexterity and problem-solving abilities. Moreover, collaborative robots can take over heavy-duty tasks. Musculoskeletal disorders (MSDs) are a serious health concern and the primary cause of absenteeism at work. While the role of the human is still essential in flexible production environment, the robot can help decreasing the workload of workers. This paper describes a novel framework for task allocation of human-robot assembly applications based on capabilities and ergonomics considerations. Capable agents are determined on the basis of agent characteristics and task requirements. Ergonomics is integrated by measuring the human body posture and the related workload. The developed framework was validated on a gearbox assembly use case using the collaborative robot Baxter.

scholarly journals Real-Time Motion Tracking for Humans and Robots in a Collaborative Assembly Task

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 48 ◽  
Author(s):  
Tadele Belay Tuli ◽  
Martin Manns
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Human Motion ◽  
Human Model ◽  
Generic Model ◽  
Primary Concern ◽  
Time Motion ◽  
Time Space ◽  
Collaborative Assembly ◽  
Human Robot Collaboration

Human-robot collaboration combines the extended capabilities of humans and robots to create a more inclusive and human-centered production system in the future. However, human safety is the primary concern for manufacturing industries. Therefore, real-time motion tracking is necessary to identify if the human worker body parts enter the restricted working space solely dedicated to the robot. Tracking these motions using decentralized and different tracking systems requires a generic model controller and consistent motion exchanging formats. In this work, our task is to investigate a concept for a unified real-time motion tracking for human-robot collaboration. In this regard, a low cost and game-based motion tracking system, e.g., HTC Vive, is utilized to capture human motion by mapping into a digital human model in the Unity3D environment. In this context, the human model is described using a biomechanical model that comprises joint segments defined by position and orientation. Concerning robot motion tracking, a unified robot description format is used to describe the kinematic trees. Finally, a concept of assembly operation that involves snap joining is simulated to analyze the performance of the system in real-time capability. The distribution of joint variables in spatial-space and time-space is analyzed. The results suggest that real-time tracking in human-robot collaborative assembly environments can be considered to maximize the safety of the human worker. However, the accuracy and reliability of the system regarding system disturbances need to be justified.

scholarly journals Seamless Human–Robot Collaborative Assembly Using Artificial Intelligence and Wearable Devices

2021 ◽  
Vol 11 (12) ◽  
pp. 5699
Author(s):  
Nikos Dimitropoulos ◽  
Theodoros Togias ◽  
Natalia Zacharaki ◽  
George Michalos ◽  
Sotiris Makris
Keyword(s):  
Search Algorithm ◽  
Manufacturing Sector ◽  
Wearable Devices ◽  
Industrial Case Study ◽  
Assembly Cell ◽  
Collaborative Assembly ◽  
Human Robot Collaboration ◽  
Collaborative Robot ◽  
High Payload

Seamless human–robot collaboration requires the equipping of robots with cognitive capabilities that enable their awareness of the environment, as well as the actions that take place inside the assembly cell. This paper proposes an AI-based system comprised of three modules that can capture the operator and environment status and process status, identify the tasks that are being executed by the operator using vision-based machine learning, and provide customized operator support from the robot side for shared tasks, automatically adapting to the operator’s needs and preferences. Moreover, the proposed system is able to assess the ergonomics in human–robot shared tasks and adapt the robot pose to improve ergonomics using a heuristics-based search algorithm. An industrial case study derived from the elevator manufacturing sector using a high payload collaborative robot is presented to demonstrate that collaboration efficiency can be enhanced through the use of the discussed system.

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

2021 ◽  
pp. 095440702110240
Author(s):  
Ziyu Zhang ◽  
Chunyan Wang ◽  
Wanzhong Zhao ◽  
Jian Feng
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Decentralized Control ◽  
Control Strategy ◽  
Centralized Control ◽  
Lateral Control ◽  
Time Performance ◽  
Collision Avoidance Control ◽  
Avoidance Control ◽  
Control Accuracy

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

scholarly journals COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

2021 ◽  
Vol 9 (4) ◽  
pp. 405
Author(s):  
Raphael Zaccone
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Open Water ◽  
Optimal Path Planning ◽  
Guidance And Control ◽  
Real Time Applications ◽  
And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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