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Mobility Strategies for Swarms of Autonomous UAVs

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HAL Id: hal-02078071

https://hal.archives-ouvertes.fr/hal-02078071

Submitted on 25 Mar 2019

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Mobility Strategies for Swarms of Autonomous UAVs

Ema Falomir, Serge Chaumette, Gilles Guerrini

To cite this version:

Ema Falomir, Serge Chaumette, Gilles Guerrini. Mobility Strategies for Swarms of Autonomous UAVs.

Journée de L’EDMI 2019, Apr 2019, Talence, France. �hal-02078071�

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Thursday April 4, 2019

https://www.labri.fr/ Twitter: @labriOfficial

Mobility Strategies for Swarms of Autonomous UAVs

Ema Falomir – Serge Chaumette, Gilles Guerrini (Thales) PROGRESS-Univ. Bordeaux, LaBRI, France

Context Related Work

Mission: detect suspicious events Without human intervention

In unknown area Quickly

vs.

Sing le dr one Sw arm

Resilient

Considered as a unique entity Quick intervention

Precision due to low altitude

Objectives Whole project

Allow Autonomous Unmanned Aerial Vehicles (UAVs) to perform collaborative tasks, such as wide area surveillance. The UAVs communicate between them and have similar characteristics: they form a swarm.

They are considered as a unique entity as seen by an operator.

PhD

Develop a distributed mobility model for a swarm of autonomous UAVs

Allow compact flights

Update the UAVs behaviour in real time, in function of the mission

objective and the airborne sensors performances

Motion Decision

Mission

Each UAV has a mission represented by its own

objective map, evolving all along the mission.

The UAVs collaborate to create a shared constraints map, updated all along the mission.

Surveillance Detection

Reconnaissance Identification

Tracking

Evolving & Local Objective Common Constraints

Collision Avoidance Embedded Sensors Information Sharing Kinematics Principle of Our Mobility Strategy

Some uses of swarms of UAVs Firemen Assistance

Pesticides Spraying Park Cleaning

Area Surveillance

Search And Rescue

Some path planning methods Artificial Potential Fields Virtual Forces

Genetic Algorithms Chaotic Processes

Particle Swarm Optimization

Dijkstra's algorithm, A* & derivatives

International Publications & Patent

E. Falomir, S. Chaumette and G.

Guerrini. Mobility Strategies based on Virtual Forces for Swarms of Autonomous UAVs in Constrained Environments. ICINCO 2017.

E. Falomir, S. Chaumette and G.

Guerrini. A Mobility Model Based on Improved Artificial Potential Fields for Swarms of UAVs. IROS 2018

E. Falomir, G. Guerrini, P. Garrec, Essaim constitué d'une pluralité de drones volants légers.

Some Illustrative Results

Simulation of:

Real environment Sensor capabilities Information sharing

Trajectories followed by a 4-UAV swarm in a 3D unknown environment Mobility Model

Distributed Dynamic

Robust to UAV loss

Obstacle Avoidance Anticipation

Our Approach:

Environment discretized into square cells

Mobility model based on Artificial Potential Field Principle (APF)

Creation of an innovative field for obstacle avoidance anticipation

UAV Goal

Obstacles on the way

Avoidance

anticipation area Path

Unknown area

Références

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