# Dynamic Path Planning of AGV Based on Kinematical Constraint A* Algorithm and Following DWA Fusion Algorithms

^{*}

## Abstract

**:**

## 1. Introduction

- (1)
- Improve the child node selection method and heuristic function of A* algorithm. This can improve the search efficiency of the algorithm. Reduce computing burden. Make the generated path more realistic;
- (2)
- For secondary redundant node removal, use B spline curve for smoothness constraint. This reduces the number of redundant points in the path. The generated path conforms to the dynamic constraints;
- (3)
- Intercept the key node information and apply improved DWA algorithm to make the local path planning follow the global path contour, so as to make the path smoother and achieve avoidance of local dynamic obstacle. It can prevent DWA algorithm from falling into local optimal. It allows the mobile robot to avoid moving obstacles.

## 2. Improved A* Algorithm

#### 2.1. Node Optimization

#### 2.2. Improved Heuristic Function

#### 2.3. Redundancy Removal

_{1}$\to $ X

_{2}$\to $ X

_{3}$\to $ X

_{4}$\to $ X

_{6}. If removing this point can shorten the length of the path, it is not tangent to the vertex of the obstacle. It is regarded as a point that can be removed and the points on the path are judged. Finally, the redundancy removal path can be obtained X

_{1}$\to $ X

_{3}$\to $ X

_{4}$\to $ X

_{5}$\to $ X

_{6}.

#### 2.4. B Spline Curve Smoothing Constraints

## 3. Improved DWA Algorithm

#### 3.1. Motion Model of Mobile Robot

#### 3.2. Speed Sampling for Mobile Robots

#### 3.3. Optimization of Evaluation Function

#### 3.4. Algorithm Fusion

## 4. Experimental and Results

## 5. Conclusions

- (1)
- In fusion algorithm, the kinematic characteristics of the mobile robot are considered. The energy efficiency of the mobile robot is not considered. This may not save energy on mobile robots;
- (2)
- The fusion algorithm in this paper does not integrate environmental awareness. It only carries out path planning on the established map. There is a need to integrate efficient perception methods.

- (1)
- The fusion algorithm is applied to a real mobile robot. Verify the feasibility of fusion algorithm in a real mobile robot. The fusion algorithm is tested on different mobile robots. Verify the compatibility of the fusion algorithm. Test the fusion algorithm in different environments. Some coefficients in the fusion algorithm are adjusted for different environments;
- (2)
- Coordinate and control robot clusters to prevent conflicts. Multi-machine collaboration can expand the working radius of the mobile robots. Autonomously assign multiple tasks. Multi-task assignment can make the mobile robots work more efficiently. Research is a hot topic.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

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**Figure 13.**(

**a**) Path of the hybrid algorithm of traditional A* algorithm and traditional DWA algorithm. (

**b**) Path of the fusion algorithm that combines kinematical constraint A* algorithm and follows DWA algorithm.

Θ | Keep 5 Directions | Abandon Direction |
---|---|---|

[337.5°, 360°) ∪ [0°, 22.5°) | 000T, 045T, 090T, 270T, 315T | 135T, 180T, 225T |

[22.5°, 67.5°) | 000T, 045T, 090T, 135T, 315T | 180T, 225T, 270T |

[67.5°, 112.5°) | 000T, 045T, 090T, 135T, 180T | 225T, 270T, 315T |

[112.5°, 157.5°) | 045T, 090T, 135T, 180T, 225T | 270T, 315T, 000T |

[157.5°, 202.5°) | 090T, 135T, 180T, 225T, 270T | 000T, 045T, 315T |

[202.5°, 247.5°) | 135T, 180T, 225T, 270T, 315T | 000T, 045T, 090T |

[247.5°, 292.5°) | 180T, 225T, 270T, 315T, 000T | 045T, 090T, 135T |

[292.5°, 337.5°) | 225T, 270T, 315T, 000T, 045T | 090T, 135T, 180T |

Map/m^{2} | Algorithm | Path Length (m) | Number of Turns | Path Time (s) |
---|---|---|---|---|

21 × 21 | Traditional A* + DWA | 35.39 | 13 | 125 |

Improved A* + DWA | 31.68 | 11 | 120 | |

Traditional A* + Traditional DWA | 20.72 | 4 | 64.5 | |

Improved A* + Improved DWA | 19.98 | 3 | 60.2 |

Map/m^{2} | Obstacles | Path Length (m) | Number of Turns | Path Time (s) |
---|---|---|---|---|

21 × 21 | dynamic obstacle | 19.93 | 3 | 59.6 |

mixed obstacles | 20.2 | 4 | 60.2 |

Algorithm | Global Optimality | Smooth Path | Local Optimality | Deceleration Obstacle Avoidance | Dynamic Obstacle Avoidance |
---|---|---|---|---|---|

Tradition A* | T | F | F | F | F |

Improved A* | T | T | F | F | F |

Hybrid Algorithm | T | T | T | T | T |

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**MDPI and ACS Style**

Yin, X.; Cai, P.; Zhao, K.; Zhang, Y.; Zhou, Q.; Yao, D.
Dynamic Path Planning of AGV Based on Kinematical Constraint A* Algorithm and Following DWA Fusion Algorithms. *Sensors* **2023**, *23*, 4102.
https://doi.org/10.3390/s23084102

**AMA Style**

Yin X, Cai P, Zhao K, Zhang Y, Zhou Q, Yao D.
Dynamic Path Planning of AGV Based on Kinematical Constraint A* Algorithm and Following DWA Fusion Algorithms. *Sensors*. 2023; 23(8):4102.
https://doi.org/10.3390/s23084102

**Chicago/Turabian Style**

Yin, Xiong, Ping Cai, Kangwen Zhao, Yu Zhang, Qian Zhou, and Daojin Yao.
2023. "Dynamic Path Planning of AGV Based on Kinematical Constraint A* Algorithm and Following DWA Fusion Algorithms" *Sensors* 23, no. 8: 4102.
https://doi.org/10.3390/s23084102