Autonomous system for data collection: Location and mapping issues in post-disaster environment

Abstract Disaster relief requires many resources. Depending on the circumstances of each event, it is important to rapidly choose the suitable means to respond to the emergency intervention. A brief review of the conditions and means demonstrated the usefulness of an autonomous stand-alone machine for these missions. If many techniques and technologies exist, their relevant combination to achieve such a system presents several challenges. This communication tries to outline the possible achievement of an autonomous vehicle under these particular circumstances. This paper focuses on the specific working conditions and welcomes future contributions from robotics and artificial intelligence. In the necessarily limited scope of this article, the authors focus on a particularly critical aspect: location. Indeed, this machine is intended to evolve in heterogeneous and dangerous environment and without any outside contacts that could last up to several days. This blackout, due to the propagation difficulties of electromagnetic waves in the ground, induces an independence of the localisation process and makes the use of any radio navigation support system (GNSS), most of the time, impossible. The knowledge of the position of the system, both for navigation of the autonomous system (Rover) and location of targets (victims buried under debris) must be able to be estimated without contributions from external systems. Inertial classical techniques, odometer, etc., have to be adapted to these conditions during a long period without external support. These techniques also have to take into account that energy optimisation requests the use of low-power processors. Consequently, only poor computing capacity is available on-board. The article starts with a presentation of the context of a post-disaster situation as well as the main missions of Search and Rescue (SaR). It is followed by the analysis of autonomous navigation located in a post-earthquake situation. We will then discuss means to determine the attitude of the autonomous system and its position. The interest of hybridisation with external systems – whenever possible –, will be evaluated with a view to correcting deviations suffered by the system during its mission. Finally, prospects and future work are presented.

[1]  Benjamin Ranft 3D perception for autonomous navigation of a low-cost MAV using minimal landmarks , 2013 .

[2]  Anthony J. Weiss,et al.  Direct position determination of narrowband radio frequency transmitters , 2004, IEEE Signal Processing Letters.

[3]  J. Bosse Géolocalisation de sources radio-électriques : stratégies, algorithmes et performances , 2012 .

[4]  Y. Roudier,et al.  Security and privacy for in-vehicle networks , 2012, 2012 IEEE 1st International Workshop on Vehicular Communications, Sensing, and Computing (VCSC).

[5]  Alexis Brenes Modélisation des phénomènes non-linéaires dans un capteur MEMS résonant pour l'optimisation de ses performances et de sa fiabilité , 2016 .

[6]  R. Persico Introduction to Ground Penetrating Radar: Inverse Scattering and Data Processing , 2014 .

[7]  Tullio Joseph Tanzi,et al.  Drone-borne GPR design: Propagation issues , 2018 .

[8]  Tullio Joseph Tanzi,et al.  Radio sciences and disaster management , 2010 .

[9]  George T Schmidt,et al.  INS/GPS Technology Trends , 2010 .

[10]  Atay Ozgovde,et al.  Heterogeneous Sensor Data Exploration and Sustainable Declarative Monitoring Architecture: Application to Smart Building , 2016 .

[12]  Jean-Luc Dugelay,et al.  Indoor Autonomous Navigation of Low-Cost MAVs Using Landmarks and 3D Perception , 2013 .

[13]  Madhu Chandra Overview of modern multi-parameter methods of radar remote sensing in context of disaster management , 2014, 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS).

[14]  Francois Lefeuvre,et al.  THE CONTRIBUTION OF RADIO SCIENCES TO DISASTER MANAGEMENT , 2011 .

[15]  Daniel Camara,et al.  TOWARDS "DRONE-BORNE" DISASTER MANAGEMENT: FUTURE APPLICATION SCENARIOS , 2016 .

[16]  George T Schmidt,et al.  INS/GPS Integration Architectures , 2010 .

[17]  Daniel Camara,et al.  Cavalry to the rescue: Drones fleet to help rescuers operations over disasters scenarios , 2014, 2014 IEEE Conference on Antenna Measurements & Applications (CAMA).

[18]  Fanilo Harivelo,et al.  Using general public connected devices for disasters victims location , 2014, 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS).

[19]  Debarati Guha-Sapir,et al.  Annual Disaster Statistical Review 2009The numbers and trends , 2010 .

[20]  Sylvie Servigne,et al.  Managing Sensor Data Uncertainty: A Data Quality Approach , 2013, Int. J. Agric. Environ. Inf. Syst..

[21]  Ludovic Apvrille,et al.  TOWARDS A NEW ARCHITECTURE FOR AUTONOMOUS DATA COLLECTION , 2015 .

[22]  Tullio Joseph Tanzi,et al.  A State of the Art of Drone (In)Security , 2017 .