Keywords
Abstract
Jointly planning paths for robots and humans in dynamic environments is considered an immensely complex task with regard to uncertainties in human motion patterns, thereby requiring real-time collision avoidance systems. Keeping in mind these challenges, there comes into existence in this publication an efficient, new model combining predictive systems for obstacle avoidance, along with hierarchical path planning to facilitate improved safety functions with optimized efficiency for collaborative robots. Utilizing the new model, there is the use of Gaussian Processes for Regression with biomechanical constraints to predict patterns in human motion with different predictive horizons in generating dynamic spatial risk maps for planning strategies for robot paths. A dual-level model, in particular, facilitates optimal trajectory planning for improved algorithms in RRT* algorithms, together with optimized Dynamic Window Approach modifications, to provide proactive collision avoidance with efficient computational processing in real-time with not more than 42.3ms planning latency in experimental analysis for UR5 in real-life collaborations for optimized safety improvements with overall success rates reaching 94.3% with optimized path-length reduction of 12.4% compared to traditional algorithms, Drawable.
References
[1] Dalmasso, M., Garrell, A., Domínguez, J. E., Jiménez, P., & Sanfeliu, A. (2021). Human-robot collaborative multi-agent path planning using Monte Carlo tree search and social reward sources. 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1-7). IEEE.
[2] He, Q., Wang, Z., Li, K., Zhang, Y., & Li, M. (2025). Research on autonomous navigation of mobile robots based on IA-DWA algorithm. Scientific Reports, 15(1), 2099.
[3] Katona, K., Neamah, H. A., & Korondi, P. (2024). Obstacle avoidance and path planning methods for autonomous navigation of mobile robot. Sensors, 24(11), 3573. https://doi.org/10.3390/s24113573
[4] Kothari, A., Tohme, T., Zhang, X., & Youcef-Toumi, K. (2025). Enhanced human-robot collaboration using constrained probabilistic human-motion prediction. 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE) (pp. 1-6). IEEE.
[5] Li, Q., Zhang, Z., You, Y., Mu, Y., & Feng, C. (2020). Data driven models for human motion prediction in human-robot collaboration. IEEE Access, 8, 227690-227702.
[6] Liu, Y., Wang, C., Wu, H., & Wei, Y. (2023). Mobile robot path planning based on kinematically constrained A-star algorithm and DWA fusion algorithm. Machines, 11(21), 4552.
[7] Smith, T., Chen, Y., Hewitt, N., Hu, B., & Gu, Y. (2023). Socially aware robot obstacle avoidance considering human intention and preferences. International Journal of Social Robotics, 15(4), 661-678.
[8] Tortora, S., Michieletto, S., Stival, F., & Menegatti, E. (2019). Fast human motion prediction for human-robot collaboration with wearable interface. 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) (pp. 1-6). IEEE.
[9] Tung, Y.-S., Luebbers, M. B., Roncone, A., & Hayes, B. (2024). Workspace optimization techniques to improve prediction of human motion during human-robot collaboration. Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (pp. 1-10).
[10] Yang, Y., Liu, J., Yang, Q., Shi, H., & Xie, Y. (2023). Path-planning for the Human-arm-like Collaborative Robot with the Capability of Infinite Rotation. 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 1-6). IEEE.
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