Distributed Conflict-Detection and Resolution Algorithms for Multiple UAVs Based on Key-Node Selection and Strategy Coordination

Abstract

A distributed conflict-detection (CD) and resolution algorithm for multiple unmanned aerial vehicles based on key-node selection and strategy coordination are proposed in this paper. Each aircraft is regarded as a critical node in a complex network. A novel temporal and spatial integrated CD methodology is proposed for aircraft formations and clusters. Each node selects three candidate strategies from a pre-set pool of strategies and generates corresponding planned tracks using uncertainty modeling based on the 6 degreesof-freedom motion. The primary combination of strategies required for coordination of the aircraft in conflict is based on the maximum-robustness principle. To address partial knowledge of the environment, a special token-allocation strategy for coordination with incomplete information is proposed in this paper. To address potential data dropouts, this study damps the solution to achieve coordination. Two extreme scenarios are constructed to examine the proposed methodology. The results showed great collision-avoidance ability in continuous conflicts with multiple aircraft in a highly dynamic 3-D environment.

A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index

“…To better describe the sizes of the aircraft trajectories and low-altitude obstacles, we base our study on an existing global longitude and latitude subdivision grid of 2 n one-dimension-integer arrays, GeoSOT (geographical coordinate grid subdivision by one-dimension-integer and two to the n-th power) [20]. GeoSOT subdivision essentially extends the longitude and latitude intervals from the eight basic grids up to 512 • (2 9• ), interpolates four levels between 1 • and 2 and another four levels between 1 and 2", and extends the part below 0.5 to 1/2048" to yield a graticule-based subdivision system of {2 8• , 2 7• , 2 6• , 2 5• , 2 4• , 2 3• , 2 2• , 2 1• , 2 0• , 2 5 , 2 4 , 2 3 , 2 2 , 2 1 , 2 0 , 2 5 ", 2 4 ", 2 3 ", 2 2 ", 2 1 ", 2 0 ", 2 −1 ", 2 −2 ", 2 −3 ", 2 −4 ", 2 −5 ", 2 −6 ", 2 −7 ", 2 −8 ", 2 −9 ", 2 −10 ", and expanding segmentation is that the 0-9 layers of grids are all integer degree segmented. In the same way, 1 • × 1 • (60' × 60') is expanded into 64' × 64', which ensures that the 9-15 level grids are all integer minutes segmented.…”

“…In fact, flight conflict detection is already a widely researched topic in the field of aviation safety. As the research in the field deepens, authors are shifting their focuses from the direct polynomial trajectory intersection of two aircrafts to the partitioning of flight regions from three-dimensional spaces to four-dimensional spaces, and from aircraft to spatiotemporal domains [4][5][6][7]. Traditional flight conflict detection methods include deterministic methods and probabilistic methods.…”

Distributed Conflict-Detection and Resolution Algorithm for UAV Swarms Based on Consensus Algorithm and Strategy Coordination

“…When multiple UAV formations encounter each other in a local airspace, the aircraft do not have the same situational awareness. A special token allocation method [20], [31] is proposed to solve the partial knowledge of the environment and coordination with incomplete information, which is the main problem of the distributed CDR algorithm.…”

“…The principle of the method is ''Do Not Interfere'' [24], which means activating a CA maneuver as late as possible. There exists a critical moment for activating a CA maneuver [31]. Before the critical moment, a pair of swarms have multiple options to avoid collision; at the critical moment, a pair of swarms can avoid collision only through simultaneously adopting optimal strategies.…”

UAV swarming with collision avoidance and communication constraints

“…In [91], a token-based distributed coordination algorithm of fixed wing aircraft was reported. Tokens based on criticality order are transmitted to adjacent neighbors on the network to determine the sequence in which agents decide on one of three pre-generated trajectories to avoid collision.…”