Assured relative and absolute navigation of a swarm of small UAS
- 1 September 2017
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)
Abstract
Small Unmanned Aerial Systems (sUAS) operations are increasing in demand and complexity. Using multiple cooperative sUAS (i.e., a swarm) can be beneficial and is sometimes necessary to perform certain tasks (e.g. precision agriculture, mapping, surveillance) either independent or collaboratively. However, controlling the flight of multiple sUAS autonomously and in real-time in a challenging environment in terms of obstacles and navigation requires highly accurate absolute and relative position and velocity information for all platforms in the swarm. This information is also necessary to effectively and efficiently resolve possible collision encounters between the sUAS. In our swarm, each platform is equipped with a Global Navigation Satellite System (GNSS) sensor, an inertial measurement unit (IMU), a baro-altimeter and a relative range sensor (range radio). When GNSS is available, its measurements are tightly integrated with IMU and baro-altimeter measurements to obtain the platform's absolute position. At the same time, the raw measurements are exchanged with the other platforms to obtain a highly accurate relative position and velocity solution with integrity. In the presence of GNSS, this relative position and velocity is used to calibrate the range radios. When GNSS is not available due to external factors (e.g., obstructions, interference), the position and velocity estimators switch to an integrated solution based on IMU, baro and relative range measurements, to maintain an accurate relative position estimate, and reduce the drift in the swarm's absolute position estimate as is typical of an IMU-based system. Multiple multi-copter data collection platforms have been developed and equipped with GNSS, inertial sensors and range radios, which were developed at Ohio University. This paper outlines the underlying methodology, the platform hardware components and analyzes and discusses sUAS flight data results.Keywords
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