An Integrated Path Planning and Tracking Framework Based on Adaptive Heuristic JPS and B-Spline Optimization

作者：Zhaoran Sun, Qiang Luo, Zhengwei Zhang, Peng Yao, Quan Liu, Shijie Zheng, Jiukun Liu · 发表于：Machines · 年份：2025 · DOI：10.3390/machines13080710 · 被引用次数：4 · 研究领域：Robotic Path Planning Algorithms、Control and Dynamics of Mobile Robots、Robotics and Sensor-Based Localization

In this paper, we propose a navigation synthesis method for indoor mobile robots based on the Improved Jumping Point Search (JPS) framework. Although traditional JPS has high search efficiency, it often leads to excessive node expansion and sharp turns in complex environments, which limits its practical application. In order to overcome these problems, we introduced three key strategies. First, we used a density-sensing heuristic function calculated by integrating the image to improve the adaptability of complex areas. Secondly, we extracted structural key points from the path and used third-order B-splines to fit them to enhance smoothness and continuity. Third, a curvature-driven Regulated Pure Pursuit (RPP) controller adjusts the look-ahead distance and speed based on path curvature, improving tracking stability. Simulation results show that the proposed method reduces planning time and node redundancy while generating smoother and more executable paths than the conventional JPS framework.