Human–Robot Collaboration Trends and Safety Aspects: A Systematic Review

Ārents, Jānis; Abolins, Valters; Judvaitis, Jānis; Vismanis, Oskars; Oraby, Aly; Ozols, Kaspars

doi:10.3390/jsan10030048

Cited by 56 publications

(27 citation statements)

References 59 publications

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“…The laparoscopic tool is inserted in the hole that simulates the incisions or the transabdominal working ports on patients. Another property of these collaborative robots is that they are safer compared to their industrial counterparts when hard collisions take place (Arents et al, 2021), but their repeatability precision is worse. However, the accuracy difference with industrial robot arms is not a critical factor for this application.…”

Section: Manipulatorsmentioning

confidence: 99%

Development of a Robotic Surgery Training System

et al. 2022

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Robotic Surgery is getting widely spread and applied to more and more clinical cases due to its advantages compared to open surgery, for both the patients and surgeons. However, Robotic Surgery requires a different set of skills and learning compared to open and also laparoscopic surgery. Tele-operation for a robotic system with hand controllers, the delay in the hand commands to be translated into robotic movements, slowness of the robotic movements, remote 2D or 3D vision of the actual operation, and lack of haptic feedback are some of the challenges that Robotic Surgery poses. Surgeons need to go through an intensive training for Robotic Surgery, and the learning and skill development continues throughout their early professional years. Despite the importance of training for Robotic Surgery, there are not yet dedicated, low-cost, and widespread training platforms; rather, surgeons mostly train with the same Robotic Surgery system they use in surgery; hence institutions need to invest on a separate surgical setup for training purposes. This is expensive for the institutions, it provides very limited access to the surgeons for training, and very limited, if any, access to researchers for experimentation. To address these, we have developed in our laboratory a low-cost, and experimental Robotic Surgery Trainer. This setup replicates the challenges that a Robotic Surgery system poses and further provides widespread access through internet connected control of the actual physical system. The overall system is composed of equipment that a standard engineering laboratory can afford. In this paper, we introduce the Robotic Surgery Training System and explain its development, parts, and functionality.

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Section: Manipulatorsmentioning

confidence: 99%

Development of a Robotic Surgery Training System

et al. 2022

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“…Nowadays, many applications have been developed to improve Human-Robot Interaction (HRI) [15], which for example, use haptic feedback devices that notify a human worker about the currently planned trajectory and changes in the status of the robot [16,17]. An essential aspect of HRI is safety [18,19], in which robot avoidance against dynamic obstacles can be addressed using dynamically changing collision volumes [20,21], and alternatively, determining the robot's speed based on detecting humans in the nearby space [22,23]. Nowadays, many applications are using neural networks [24,25] for detecting humans and predicting their movement [26,27].…”

Section: Introductionmentioning

confidence: 99%

Distributed Camera Subsystem for Obstacle Detection

Oščádal

Spurný

Kot

et al. 2022

Sensors

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This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras.

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“…Nowadays robots are being used in various applications within health care [1], agriculture [2], food preparation [3], industrial machinery [4], manufacturing [5], military [6] or in a collaboration with humans [7] and other sectors. To tackle today's and future challenges robots require a distinct set of skills to perceive and analyse the environment and act accordingly [8].…”

Section: Introductionmentioning

confidence: 99%

Construction and benchmark of an autonomous tracked mobile robot system

Ārents¹,

Ahluwalia²,

Oraby³

et al. 2022

Robot. syst., appl.

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Robots require a certain set of skills to perceive and analyse the environment and act accordingly. For tracked mobile robots getting good odometry data from sensory information is a challenging key prerequisite to perform in an unstructured dynamic environment, thus an essential issue in the tracked mobile robotics domain. In this article, we construct a ROS-based tracked mobile robot system taking the Jaguar V4 mobile robot as the base platform. On which several visual odometry solutions based on different cameras and methods (Intel RealSense T265, Zed camera, RTAB-Map RGBD) are integrated and benchmark comparison is performed. Analysis of new challenges faced by different methods while applied on a tracked vehicle as well as recommendations and conclusions are presented. Intel RealSense T265 solution proved to perform well in uncertain conditions which involves bounded vibrations and low lighting conditions with low latency, which result in good map generation. Further evaluations with a path planning algorithm and Intel RealSense T265 were conducted to test the effect of the robot's motion profiles on odometry data accuracy.

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Human–Robot Collaboration Trends and Safety Aspects: A Systematic Review

Cited by 56 publications

References 59 publications

Development of a Robotic Surgery Training System

Development of a Robotic Surgery Training System

Distributed Camera Subsystem for Obstacle Detection

Construction and benchmark of an autonomous tracked mobile robot system

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