Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments
- First, we propose 3D-SWAP, a novel algorithm for 3D collision avoidance with multiple UAVs. The algorithm extends ideas from our previous work on ground robots that swap their positions in a traffic roundabout fashion. Here, a similar strategy on a horizontal plane is combined with a control of the UAVs’ altitude to navigate safely in 3D environments. Thus, UAVs that are far enough in altitude can ignore each other, making the swapping of the rest more efficient. Moreover, our approach requires low computational load, is decentralized and works with noisy sensors and restricted communication.
- Second, we detail our system architecture and the implementation of our method in a real team of UAVs. We tested our algorithm in realistic simulations to assess its performance. Later, we also run tests in outdoor field experiments, coping with noisy communication, inaccurate positioning systems, wind gusts, etc. We explain our procedures for the development and integration of the algorithm in these field experiments.
2. Related Work
3. Problem Description
- Holonomic vehicles. We model UAVs as holonomic vehicles (e.g., multirotors). They can move in any direction independently from their yaw orientation. We assume that their acceleration and speed constraints allow them to stop horizontally within a planar breaking distance , and stop their vertical movement within a vertical distance .
- Noisy localization. UAVs can localize themselves by means of noisy sensors. In outdoor scenarios, UAVs could carry GPS receivers and altimeters, for instance. Each UAV has access to its own noisy localization , such that and . and are the maximum localization errors on the xy-plane and altitude, respectively. We differentiate them, as altitude is usually more precise due to the use of altimeters or lasers.
- Local communication. If two UAVs i and j are within communication range, i.e., , they can exchange their noisy localizations and . Thus, UAVs share with their neighbors their position, but not their goals, velocities nor orientations. We do not assume a perfect communication. When communication links fail, other UAVs could still be detected with the onboard sensors.
- Obstacle detection. UAVs have onboard sensors to detect obstacles within a 3D distance . In particular, we assume that each UAV has a 3D sensor generating poincloud-based measurements from the obstacles around (e.g., a Lidar). Each pointcloud consists of M points, where each point m, without losing generality, can be expressed in cylindrical coordinates relative to the UAV . Again, we assume those measurements to be noisy.
4.1. Overview and Preliminaries
|Algorithm 1 3D-SWAP for each UAV i|
|Input: Pointcloud from local sensors, current position , goal position |
4.2. The Cylindrical Obstacle Diagram
4.3. Avoidance Maneuvers
5.2. Optimality and Robustness
6. System Integration and Experiments
6.1. Aerial Platforms
6.2. System Integration
6.4. Field Experiments
6.4.1. Tuning Parameters
Conflicts of Interest
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Ferrera, E.; Alcántara, A.; Capitán, J.; Castaño, A.R.; Marrón, P.J.; Ollero, A. Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments. Sensors 2018, 18, 4101. https://doi.org/10.3390/s18124101
Ferrera E, Alcántara A, Capitán J, Castaño AR, Marrón PJ, Ollero A. Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments. Sensors. 2018; 18(12):4101. https://doi.org/10.3390/s18124101Chicago/Turabian Style
Ferrera, Eduardo, Alfonso Alcántara, Jesús Capitán, Angel R. Castaño, Pedro J. Marrón, and Aníbal Ollero. 2018. "Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments" Sensors 18, no. 12: 4101. https://doi.org/10.3390/s18124101