GPS-Free, Error Tolerant Path Planning for Swarms of Micro Aerial Vehicles with Quality Amplification

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 14; pp. 4731 - 4731:16
• Main Authors: Barbeau, Michel, Garcia-Alfaro, Joaquin, Kranakis, Evangelos, Santos, Fillipe
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.07.2021; MDPI
• Subjects: Algorithms; autonomous aerial vehicles; Cameras; Classification; Collaboration; Communication; Computer Science; Cooperation; Cryptography and Security; Data analysis; Drones; goal location; information sharing; Localization; MAV swarm; micro aerial vehicles (MAVs); quadcopters; Sensors; localization; autonomous aerial vehicles; information sharing; quadcopters; MAV swarm; location; path planning; goal location; micro aerial vehicles (MAVs); Information sharing; Autonomous aerial vehicles; Path planning; Quadcopters; Localization; Goal location; Location; Micro aerial vehicles (MAVs)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21144731

We present an error tolerant path planning algorithm for Micro Aerial Vehicle (MAV) swarms. We assume navigation without GPS-like techniques. The MAVs find their path using sensors and cameras, identifying and following a series of visual landmarks. The visual landmarks lead the MAVs towards their destination. MAVs are assumed to be unaware of the terrain and locations of the landmarks. They hold a priori information about landmarks, whose interpretation is prone to errors. Errors are of two types, recognition or advice. Recognition errors follow from misinterpretation of sensed data or a priori information, or confusion of objects, e.g., due to faulty sensors. Advice errors are consequences of outdated or wrong information about landmarks, e.g., due to weather conditions. Our path planning algorithm is cooperative. MAVs communicate and exchange information wirelessly, to minimize the number of recognition and advice errors. Hence, the quality of the navigation decision process is amplified. Our solution successfully achieves an adaptive error tolerant navigation system. Quality amplification is parameterized with respect to the number of MAVs. We validate our approach with theoretical proofs and numeric simulations.