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Recent advances in robotics have enabled robots to collaborate with workers in shared, fenceless workplaces in construction and civil engineering, which can improve productivity and address labor shortages. However, this collaboration may lead to collisions between workers and robots. Targeting safe collaboration, this study proposes an intention‐aware motion planning method for robots to avoid collisions. This method involves two novel deep networks that allow robots to anticipate the motions of workers based on inferences about workers' motion intentions. Then, a probabilistic collision‐checking mechanism is developed that enables robots to estimate the collision probability with the motions of workers and generate collision‐free adjustments. The results verify that the method enables robots to predict workers' intended motions 1 s in advance and generate adjustments with a collision probability of less than 5.0% during collaborative masonry tasks. This study facilitates the safe implementation of collaborative robots in construction and civil engineering.
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Evaluation of Machine Learning Algorithms for Worker’s Motion Recognition Using Motion Sensors
Construction tasks involve various activities composed of one or more body motions. It is essential to understand the dynamically changing behavior and state of construction workers to manage construction workers effectively with regards to their safety and productivity. While several research efforts have shown promising results in activity recognition, further research is still necessary to identify the best locations of motion sensors on a worker’s body by analyzing the recognition results for improving the performance and reducing the implementation cost. This study proposes a simulation-based evaluation of multiple motion sensors attached to workers performing typical construction tasks. A set of 17 inertial measurement unit (IMU) sensors is utilized to collect motion sensor data from an entire body. Multiple machine learning algorithms are utilized to classify the motions of the workers by simulating several scenarios with different combinations and features of the sensors. Through the simulations, each IMU sensor placed in different locations of a body is tested to evaluate its recognition accuracy toward the worker’s different activity types. Then, the effectiveness of sensor locations is measured regarding activity recognition performance to determine relative advantage of each location. Based on the results, the required number of sensors can be reduced maintaining the recognition performance. The findings of this study can contribute to the practical implementation of activity recognition using simple motion sensors to enhance the safety and productivity of individual workers.
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- Award ID(s):
- 1919068
- PAR ID:
- 10157780
- Date Published:
- Journal Name:
- Computing in Civil Engineering 2019
- Page Range / eLocation ID:
- 51 to 58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784482438.007
