Asmaa Loulou, Mustafa Unel, Hybrid attention-guided RRT*: Learning spatial sampling priors for accelerated path planning, Robotics and Autonomous Systems, Volume 198, 2026, 10.1016/j.robot.2026.105338.
See also: the no so strong influence of time in some cognitive processes, such as speech processing (https://doi.org/10.1016/j.tics.2025.05.017)Sampling-based planners such as RRT* are widely used for motion planning in high-dimensional and complex environments. However, their reliance on uniform sampling often leads to slow convergence and inefficiency, especially in scenarios with narrow passages or long-range dependencies. To address this, we propose HAGRRT*, a Hybrid Attention-Guided RRT* algorithm that learns to generate spatially informed sampling priors. Our method introduces a new neural architecture that fuses multi-scale convolutional features with a lightweight cross-attention mechanism, explicitly conditioned on the start and goal positions. These features are decoded via a DPT-inspired module to produce 2D probability maps that guide the sampling process. Additionally, we propose an obstacle-aware loss function that penalizes disconnected and infeasible predictions which further encourages the network to focus on traversable, goal-directed regions. Extensive experiments on both structured (maze) and unstructured (forest) environments show that HAGRRT* achieves significantly faster convergence and improved path quality compared to both classical RRT* and recent deep-learning guided variants. Our method consistently requires fewer iterations and samples and is able to generalize across varying dataset types. On structured scenarios, our method achieves an average reduction of 39.6% in the number of samples and an average of 24.4% reduction in planning time compared to recent deep learning methods. On unstructured forest maps, our method reduces the number of samples by 71.5%, and planning time by 81.7% compared to recent deep learning methods, and improves the success rate from 67% to 93%. These results highlight the robustness, efficiency, and generalization ability of our approach across a wide range of planning environments.