Tuning Perception and Motion Planning Parameters for Moveit! Framework

Grushko, Stefan; Vysocký, Aleš; Jha, Vyomkesh Kumar; Pastor, Robert Α.; Prada, Erik; Miková, Ľubica; Bobovský, Zdenko

doi:10.17973/mmsj.2020_11_2020064

Cited by 3 publications

(2 citation statements)

References 5 publications

(8 reference statements)

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“…In addition to direct robot control, human-robot communication can also encompass safety features. For example, the robot may detect the position of a human and avoid any potential collision [9,10]. Voice commands allow for a wider range of instructions to be programmed compared to gestures, but in noisy industrial environments, voice commands may be distorted and not correctly recognised.…”

Section: Communication Strategies For Intuitive Interactionmentioning

confidence: 99%

Hand Gesture Interface for Robot Path Definition in Collaborative Applications: Implementation and Comparative Study

Vysocký

Poštulka

Chlebek

et al. 2023

Sensors

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The article explores the possibilities of using hand gestures as a control interface for robotic systems in a collaborative workspace. The development of hand gesture control interfaces has become increasingly important in everyday life as well as professional contexts such as manufacturing processes. We present a system designed to facilitate collaboration between humans and robots in manufacturing processes that require frequent revisions of the robot path and that allows direct definition of the waypoints, which differentiates our system from the existing ones. We introduce a novel and intuitive approach to human–robot cooperation through the use of simple gestures. As part of a robotic workspace, a proposed interface was developed and implemented utilising three RGB-D sensors for monitoring the operator’s hand movements within the workspace. The system employs distributed data processing through multiple Jetson Nano units, with each unit processing data from a single camera. MediaPipe solution is utilised to localise the hand landmarks in the RGB image, enabling gesture recognition. We compare the conventional methods of defining robot trajectories with their developed gesture-based system through an experiment with 20 volunteers. The experiment involved verification of the system under realistic conditions in a real workspace closely resembling the intended industrial application. Data collected during the experiment included both objective and subjective parameters. The results indicate that the gesture-based interface enables users to define a given path objectively faster than conventional methods. We critically analyse the features and limitations of the developed system and suggest directions for future research. Overall, the experimental results indicate the usefulness of the developed system as it can speed up the definition of the robot’s path.

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Section: Communication Strategies For Intuitive Interactionmentioning

confidence: 99%

Hand Gesture Interface for Robot Path Definition in Collaborative Applications: Implementation and Comparative Study

Vysocký

Poštulka

Chlebek

et al. 2023

Sensors

Self Cite

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“…There are already functional tools for such an application, such as MoveIt! [28], implemented in the ROS (Robotic Operating System) [29] environment. MoveIt!…”

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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