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This research advances autonomous flight planning by introducing asymmetric climb rate constraints into Dubins airplane path algorithms, enabling fixed-wing vehicles to achieve significantly faster transit times over rugged terrain. By preserving mathematical optimality while allowing distinct performance characteristics for climbing and descending phases, the method unlocks up to 71% reductions in minimum travel time compared to symmetric models validated through real-world flight tests.
Minimum Time Dubins Airplane Paths with Asymmetric Climb Rates
arXiv:2607.15863v1 Announce Type: new Abstract: Dubins airplane paths approximate the limited maneuverability of fixed-wing vehicles with minimum curvature and climb rate constraints. However, the symmetric climb rate constraints result in sub-optimal paths and conservative vehicle performance. In this work, we propose asymmetric Dubins airplane paths, which consider asymmetric climb rates for climbing and descending. We revisit the time optimality conditions and show that the asymmetric flight path angle constraints preserve optimality. We show that by considering asymmetric climb rates, we can take advantage of full performance of the vehicle, reducing the minimum time by 71% for connecting randomly generated states. We also demonstrate that the added climb rate results in 2.8 times faster to find the median solution time when integrated into a sampling-based planning task on rugged terrain, due to the added feasibility. We further demonstrate the practicality of the approach with a real-world flight.
Abstract:Dubins airplane paths approximate the limited maneuverability of fixed-wing vehicles with minimum curvature and climb rate constraints. However, the symmetric climb rate constraints result in sub-optimal paths and conservative vehicle performance. In this work, we propose asymmetric Dubins airplane paths, which consider asymmetric climb rates for climbing and descending. We revisit the time optimality conditions and show that the asymmetric flight path angle constraints preserve optimality. We show that by considering asymmetric climb rates, we can take advantage of full performance of the vehicle, reducing the minimum time by 71% for connecting randomly generated states. We also demonstrate that the added climb rate results in 2.8 times faster to find the median solution time when integrated into a sampling-based planning task on rugged terrain, due to the added feasibility. We further demonstrate the practicality of the approach with a real-world flight.
Robotics (cs.RO)
arXiv:2607.15863 [cs.RO]
(or arXiv:2607.15863v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.15863 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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