ICUAS 2020 Paper Abstract

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Paper ThB1.5

Madridano, Angel (Universidad Carlos III de Madrid), Al-Kaff, Abdulla (Universidad Carlos III de Madrid), Flores Peña, Pablo (Drone Hopper S.L), Martín Gómez, David (Universidad Carlos III de Madrid), de La Escalera, Arturo (Universidad Carlos III de Madrid)

Obstacle Avoidance Manager for UAVs Swarm

Scheduled for presentation during the Regular Session "See and Avoid Systems II" (ThB1), Thursday, September 3, 2020, 16:20−16:40, Macedonia Hall

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on September 25, 2020

Keywords See-and-avoid Systems, Swarms, Path Planning

Abstract

Multi-Robot Systems for a swarm of autonomous UAVs are critical in providing essential solutions to numerous applications. However, working in complex environments requires higher levels of safety in navigation. One of the problems is the detection and avoidance of static and dynamic obstacles that appear in the UAVs paths.

In this paper, two complementary methods are presented; to provide safe autonomous navigation of a swarm of UAVs. On the one hand, a method based on the Velocity Obstacle (VO) is responsible for avoiding collisions between the UAVs of the swarm by decreasing the velocity of the UAVs when they approach the same location. This method has the advantage of reducing the use of UAV resources and computational time. On the other hand, a method based on Light Detection and Ranging (LiDAR) information and Probabilistic Roadmaps (PRM) allows detecting dynamic obstacles appear in the path, exploring the environment in search of alternative paths, and finally establishing the one that minimizes the distance, and allows reaching a location avoiding the detected obstacles. The proposed methods have been tested and validated in simulation environments; as a previous step to their implementation in real UAVs. Moreover, the obtained results show the robustness and the efficiency of the system in detecting and avoiding the possible collisions.

 

 

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