Abstract
Aiming at the problems of collision, non-smoothness, and discontinuity of the trajectory generated by the motion planning of the service robot in the home environment, this paper proposes a robot motion planning system consisting of three parts: path planning, trajectory generation, and trajectory optimization. First, use the A* algorithm based on graph search to quickly plan a passable global path in a complex home environment as the initial value of trajectory generation; secondly, construct an objective function based on Minimum Snap to generate the initial trajectory to be optimized; finally, Using the convex hull property of the Bezier curve, the safety corridor is constructed and the time distribution is adjusted by the trapezoidal velocity curve method, which solves the overshoot phenomenon that occurs in the solution process of the trajectory generation. The Minimum Snap method based on the Bezier Curve is constructed to optimize the trajectory and finally generate A continuous and smooth motion trajectory with minimal energy loss suitable for service robots. The feasibility and effectiveness of this method are proved by simulation experiments.
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References
Tagliavini, L., et al.: On the suspension design of paquitop, a novel service robot for home assistance applications. Machines 9(3), 52 (2021)
Guruji, A.K., Agarwal, H., Parsediya, D.K.: Time-efficient A* algorithm for robot path planning. Procedia Technol. 23, 144–149 (2016)
Yang, R.J., Cheng, L.: Path planning of restaurant service robot based on a-star algorithms with updated weights. In: 2019 12th International Symposium on Computational Intelligence and Design (ISCID), pp. 292–295. IEEE, Hangzhou (2019)
Ferguson, D., Stentz, A.: Using interpolation to improve path planning: the field D* algorithm. J. Field Robot. 23(2), 79–101 (2006)
Wang, W., et al.: An improved RRT path planning algorithm for service robot. In: 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1824–1828. IEEE, Chongqing (2020)
Zhou, J., et al.: Pushing revisited: differential flatness, trajectory planning, and stabilization. Int. J. Robot. Res. 38(12–13), 1477–1489 (2019)
Shomin, M., et al.: Fast, dynamic trajectory planning for a dynamically stable mobile robot. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3636–3641. IEEE, Illinois (2014)
Preiss, J.A., et al.: Downwash-aware trajectory planning for large quadrotor teams. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 250–257. IEEE, Vancouver (2017)
Mellinger, D., et al.: Minimum snap trajectory generation and control for quadrotors. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2520–2525. IEEE, Shanghai (2011)
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Shu, Y., Zhao, D., Yang, Z., Yang, J., Hiroshi, Y. (2022). Motion Planning Method for Home Service Robot Based on Bezier Curve. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13455. Springer, Cham. https://doi.org/10.1007/978-3-031-13844-7_8
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DOI: https://doi.org/10.1007/978-3-031-13844-7_8
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