Abstract
Planning and navigation of mobile robots has always been a challenging problem, which has attracted a large number of scholars, especially the research on local path planners. In order to use the past planning experience to guide future path planning, a memory-based stochastic trajectory optimization for motion planning (M-STOMP) is used to solve the local path planning problem. Firstly, the past path planning experience is continuously used to guide the subsequent planning by using memory, which is a method for continuous planning. Then, STOMP algorithm uses Gaussian distribution to generate some smooth paths in the state space, and uses optimized method to update to get a better path. Finally, this method was tested in four different scenarios which validate the proposed method. This paper gave a method for local path planning from a new perspective.
This work is supported by State Grid Tianjin electric power company science and technology project (KJ21-1-32).
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Li, W., Cao, T., Wang, Y., Guo, X. (2022). Memory-Based STOMP for Local Path Planning. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_53
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