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arXiv · 2508.21549

Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler

Liding Zhang·Kuanqi Cai·Yu Zhang·Zhenshan Bing·Chaoqun Wang·Fan Wu·Sami Haddadin·Alois Knoll
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Abstract

Path planning in robotics often involves solving continuously valued, high-dimensional problems. Popular informed approaches include graph-based searches, such as A*, and sampling-based methods, such as Informed RRT*, which utilize informed set and anytime strategies to expedite path optimization incrementally. Informed sampling-based planners define informed sets as subsets of the problem domain based on the current best solution cost. However, when no solution is found, these planners re-sample and explore the entire configuration space, which is time-consuming and computationally expensive. This article introduces Multi-Informed Trees (MIT*), a novel planner that constructs estimated informed sets based on prior admissible solution costs before finding the initial solution, thereby accelerating the initial convergence rate. Moreover, MIT* employs an adaptive sampler that dynamically adjusts the sampling strategy based on the exploration process. Furthermore, MIT* utilizes length-related adaptive sparse collision checks to guide lazy reverse search. These features enhance path cost efficiency and computation times while ensuring high success rates in confined scenarios. Through a series of simulations and real-world experiments, it is confirmed that MIT* outperforms existing single-query, sampling-based planners for problems in R^4 to R^16 and has been successfully applied to real-world robot manipulation tasks. A video showcasing our experimental results is available at: https://youtu.be/30RsBIdexTU

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Liding Zhang, Kuanqi Cai, Yu Zhang, Zhenshan Bing, Chaoqun Wang, Fan Wu, Sami Haddadin, Alois Knoll. 2025-08-29. Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler. https://doi.org/10.1109/tase.2025.3590084

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