Abstract

In recent years, laser radar (LIDAR) has become a promising technology for navigation and obstacle avoidance in helicopters and UAV, mainly because of its good wire detection performance on a wide range of incidence angles, and also due to its outstanding range and accuracy. In this paper we describe the activities carried out for the design, integration and test of the Laser Obstacle Avoidance System “Marconi” (LOAM) on helicopter and UAV platforms. After a brief description of the system architecture and sensor characteristics, emphasis is given to the performance models and processing algorithms required for obstacle detection/classification and calculation of alternative flight paths, as well as to the ground and flight test activities performed on various platforms.

Highlights

  • In order to achieve mission effectiveness in the present threat environment, military helicopters and small Unmanned Aerial Vehicles (UAVs) operations are focusing on low-level or nap-of-the-earth flying

  • In this paper we describe the activities carried out for the design, integration and test of the Laser Obstacle Avoidance System “Marconi” (LOAM) on helicopter and UAV platforms

  • After a brief description of the system architecture and sensor characteristics, emphasis is given to the performance models and processing algorithms required for obstacle detection/classification and calculation of alternative flight paths, as well as to the ground and flight test activities performed on various platforms

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Summary

Introduction

In order to achieve mission effectiveness in the present threat environment, military helicopters and small Unmanned Aerial Vehicles (UAVs) operations are focusing on low-level or nap-of-the-earth flying. After a brief description of the system architecture and sensor characteristics, emphasis is given to the performance models and processing algorithms required for obstacle detection/classification and calculation of alternative flight paths, as well as to the ground and flight test activities performed on various platforms.

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