An Enhanced Informed-RRT* Integrating Bidirectional Exploration, Fast Sampling, and Local Path Optimization - Heru Suwoyo; Taufik Hidayat; Yingzhong Tian; Julpri Andika; Andi Adriansyah (Journal of Embedded Systems, Security and Intelligent Systems, 2026)
Purpose – This study aims to improve the efficiency and path quality of Informed-RRT* for mobile robot path planning in complex environments by integrating bidirectional exploration, fast sampling, and local path optimization. Design/methods/approach – The proposed method follows a two-stage framework. In the initial exploration phase, two search trees are expanded bidirectionally using fast sampling to accelerate feasible path discovery. An ancestor-based parent selection mechanism based on the triangular inequality principle is then applied to improve local path quality and reduce inefficient rewiring. After an initial path is found, the algorithm switches to an informed optimization phase using ellipsoidal sampling while maintaining simultaneous two-tree expansion. The proposed method was evaluated in three two-dimensional static environments with different levels of complexity and compared with Fast-RRT* and Informed-RRT*. Performance was assessed using path cost, computation time, number of iterations, and Wilcoxon signed-rank testing. Findings – The results show that the proposed method consistently achieves lower path cost, shorter computation time, and fewer iterations than the baseline methods. Across simple to maze environments, the method reduces computation time by approximately 24–75% and improves path optimality by approximately 3.9–4.8%. Statistical testing confirms significant improvements in more constrained and maze-like environments. Research implications/limitations – The study demonstrates that combining accelerated exploration and local optimization can improve sampling-based path planning performance. However, the evaluation is limited to two-dimensional static environments and selected baseline methods. Originality/value – The proposed framework provides an enhanced Informed-RRT* approach that jointly addresses initial exploration efficiency, local path refinement, and informed optimization within a unified planning strategy.