# LiDAR Based Detect and Avoid System for UAV Navigation in UAM Corridors

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. LiDAR Simulation

#### 2.2. SOCP Collision Avoidance Algorithm

#### 2.2.1. Navigation Environment of the UAV

#### 2.2.2. SOCP Formulation

## 3. Results

#### 3.1. LiDAR System Detection Capabilities

#### 3.2. Avoidance Maneuvers

#### 3.2.1. Colinear Obstacle

#### 3.2.2. Perpendicular Obstacle

#### 3.2.3. Obstacle with Acceleration

^{2}to avoid the collision. After this change in the trajectory of the obstacle, if the UAV were to perform the maneuver calculated in Section 3.2.2, it would violate the minimum safety distance as represented in (Figure 9b).

#### 3.2.4. Dynamic Scenario

#### 3.3. Computation Time

## 4. Conclusions

- The position and speed of the obstacles were correctly measured employing the point clouds from the LiDAR sensor.
- UAV operational characteristics are considered for the computation of trajectories.
- A fast implementation was obtained that allows the calculation of trajectories practically in real time on a modern computer.

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 8.**Perpendicular obstacle avoidance maneuver: (

**a**) Scheduled trajectory. (

**b**) Recalculated path.

**Figure 9.**Accelerating obstacle avoidance maneuver: (

**a**) Initial forecasted obstacle trajectory. (

**b**) Initial avoidance maneuver.

Characteristic | Specification |
---|---|

Detection Range (@100 klx) | 190 m @ 10% reflectivity |

230 m @ 20% reflectivity | |

320 m @ 80% reflectivity | |

Field of view | 70.4° (Horizontal) × 77.2° (Vertical) |

Range Precision (1σ @ 20 m) | 2 cm |

Point Rate | 240,000 pts/s |

Weight | 498 g |

Dimensions | 91 × 61.2 × 64.8 mm |

Distance (m) | Real Speed (m/s) | ${\mathit{a}}_{\mathit{i}}\text{}\left(\mathbf{m}\right)$ | ${\mathit{b}}_{\mathit{i}}\text{}(\mathbf{m}/\mathbf{s})$ |
---|---|---|---|

10 | −2 | 9.94 ± 0.03 | −2.01 ± 0.02 |

10 | −5 | 9.94 ± 0.03 | −5.00 ± 0.05 |

10 | −10 | 9.96 ± 0.03 | −9.96 ± 0.05 |

30 | −2 | 29.95 ± 0.03 | −2.06 ± 0.13 |

30 | −5 | 29.97 ± 0.03 | −5.03 ± 0.14 |

30 | −10 | 29.95 ± 0.03 | −10.02 ± 0.12 |

Study Case | Discretization Steps | Solver | Computation Time (s) |
---|---|---|---|

Colinear Obstacle | IPOPT | 0.11 | |

50 | SNOPT | 0.07 | |

KNITRO | 0.04 | ||

Perpendicular Obstacle | IPOPT | 0.05 | |

50 | SNOPT | 0.04 | |

KNITRO | 0.03 | ||

Obstacle with acceleration | IPOPT | 0.04 | |

50 | SNOPT | 0.03 | |

KNITRO | 0.03 | ||

Dynamic Scenario (First maneuver) | IPOPT | 0.08 | |

50 | SNOPT | 0.04 | |

KNITRO | 0.04 | ||

Dynamic Scenario (Second maneuver) | IPOPT | 0.05 | |

39 | SNOPT | 0.03 | |

KNITRO | 0.03 | ||

Dynamic Scenario (Third maneuver) | IPOPT | 0.04 | |

22 | SNOPT | 0.03 | |

KNITRO | 0.03 |

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**MDPI and ACS Style**

Aldao, E.; González-de Santos, L.M.; González-Jorge, H.
LiDAR Based Detect and Avoid System for UAV Navigation in UAM Corridors. *Drones* **2022**, *6*, 185.
https://doi.org/10.3390/drones6080185

**AMA Style**

Aldao E, González-de Santos LM, González-Jorge H.
LiDAR Based Detect and Avoid System for UAV Navigation in UAM Corridors. *Drones*. 2022; 6(8):185.
https://doi.org/10.3390/drones6080185

**Chicago/Turabian Style**

Aldao, Enrique, Luis M. González-de Santos, and Higinio González-Jorge.
2022. "LiDAR Based Detect and Avoid System for UAV Navigation in UAM Corridors" *Drones* 6, no. 8: 185.
https://doi.org/10.3390/drones6080185