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<p>In this work, we propose a rapid trajectory planning method based on robotic kinematic constraints, which is specifically applied to ground-based robot navigation in unknown environments. We address this task by formulating a quadratically constrained quadratic programming problem that integrates third-order polynomials and multiple motion states to generate trajectories while considering kinematic constraints. The solutions derived from this method are utilized in initial path point generation. Leveraging the geometric properties of convex decomposition enables path point corrections, while employing Bézier curves enhances trajectory smoothness. We introduce a novel replanning framework encompassing local waypoint generation, trajectory collision detection, and replanning strategies. The viability and effectiveness of our method are demonstrated through simulations and real-world experiments.</p>

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Rapid trajectory planning under multiple constraints in unknown environments

  • 期刊论文

  • 发表日期：

    2025年3月14日

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  • Chenghong Xu,
  • Wenjie Qi,
  • Zhigang Zhang,
  • Shuaishuai Ge,
  • Xiangkui Zhang,
  • Guojian Wang,
  • Fei Peng

摘要

In this work, we propose a rapid trajectory planning method based on robotic kinematic constraints, which is specifically applied to ground-based robot navigation in unknown environments. We address this task by formulating a quadratically constrained quadratic programming problem that integrates third-order polynomials and multiple motion states to generate trajectories while considering kinematic constraints. The solutions derived from this method are utilized in initial path point generation. Leveraging the geometric properties of convex decomposition enables path point corrections, while employing Bézier curves enhances trajectory smoothness. We introduce a novel replanning framework encompassing local waypoint generation, trajectory collision detection, and replanning strategies. The viability and effectiveness of our method are demonstrated through simulations and real-world experiments.