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Fast Safe Rectangular Corridor-based Online AGV Trajectory Optimization with Obstacle Avoidance

Shaoqiang Liang, Songyuan Fa, Yiqun Li

TL;DR

This work proposes an efficient trajectory planning framework for AGVs by formulating the problem as an optimal control problem, and utilizes the fast safe rectangular corridor (FSRC) algorithm to construct rectangular convex corridors, representing avoidance constraints as box constraints.

Abstract

Automated Guided Vehicles (AGVs) are essential in various industries for their efficiency and adaptability. However, planning trajectories for AGVs in obstacle-dense, unstructured environments presents significant challenges due to the nonholonomic kinematics, abundant obstacles, and the scenario's nonconvex and constrained nature. To address this, we propose an efficient trajectory planning framework for AGVs by formulating the problem as an optimal control problem. Our framework utilizes the fast safe rectangular corridor (FSRC) algorithm to construct rectangular convex corridors, representing avoidance constraints as box constraints. This eliminates redundant obstacle influences and accelerates the solution speed. Additionally, we employ the Modified Visibility Graph algorithm to speed up path planning and a boundary discretization strategy to expedite FSRC construction. Experimental results demonstrate the effectiveness and superiority of our framework, particularly in computational efficiency. Compared to advanced frameworks, our framework achieves computational efficiency gains of 1 to 2 orders of magnitude. Notably, FSRC significantly outperforms other safe convex corridor-based methods regarding computational efficiency.

Fast Safe Rectangular Corridor-based Online AGV Trajectory Optimization with Obstacle Avoidance

TL;DR

This work proposes an efficient trajectory planning framework for AGVs by formulating the problem as an optimal control problem, and utilizes the fast safe rectangular corridor (FSRC) algorithm to construct rectangular convex corridors, representing avoidance constraints as box constraints.

Abstract

Automated Guided Vehicles (AGVs) are essential in various industries for their efficiency and adaptability. However, planning trajectories for AGVs in obstacle-dense, unstructured environments presents significant challenges due to the nonholonomic kinematics, abundant obstacles, and the scenario's nonconvex and constrained nature. To address this, we propose an efficient trajectory planning framework for AGVs by formulating the problem as an optimal control problem. Our framework utilizes the fast safe rectangular corridor (FSRC) algorithm to construct rectangular convex corridors, representing avoidance constraints as box constraints. This eliminates redundant obstacle influences and accelerates the solution speed. Additionally, we employ the Modified Visibility Graph algorithm to speed up path planning and a boundary discretization strategy to expedite FSRC construction. Experimental results demonstrate the effectiveness and superiority of our framework, particularly in computational efficiency. Compared to advanced frameworks, our framework achieves computational efficiency gains of 1 to 2 orders of magnitude. Notably, FSRC significantly outperforms other safe convex corridor-based methods regarding computational efficiency.
Paper Structure (14 sections, 4 equations, 11 figures, 4 tables, 3 algorithms)

This paper contains 14 sections, 4 equations, 11 figures, 4 tables, 3 algorithms.

Table of Contents

  1. INTRODUCTION
  2. Related Works
  3. Motivations, Contributions, and Organization
  4. PROBLEM FORMULATION
  5. Formulation of Trajectory Planning Problem
  6. Discrete Formulation of Trajectory Planning Problem
  7. ALGORITHM
  8. Discretization Strategy for Obstacle Boundariesn
  9. Modified Visibility Graph
  10. Fast Safe Rectangular Corridor
  11. EXPERIMENT RESULTS AND DISCUSSIONS
  12. Simulation
  13. PHYSICAL EXPERIMENT
  14. CONCLUSIONS

Figures (11)

  • Figure 1: Illustration of safe convex corridor-based methods: the red rectangles represent the constructed convex corridors, with each trajectory point corresponding to a rectangle. The light orange areas indicate redundant obstacles.
  • Figure 2: The kinematic model of the AGV.
  • Figure 3: Inflation of Obstacles and discretization
  • Figure 4: Results of the Modified Visibility Graph. (Enhance visibility for finer details within the figure by zooming in.)
  • Figure 5: The creation process of FSRC: (a) path points and obstacle set $\Lambda$, (b) initial bounding box $\mathcal{B_R}(i,:)$, (c) expansion in four directions, (d) expansion encountering obstacles.
  • ...and 6 more figures